Adsorption, Ion Exchange and Catalysis: Design of Operations and Environmental Applications (2006)

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Preface In this book, three important processes, namely, adsorption, ion exchange, and heterogeneous catalysis, are presented along with environmental issues. Specifically, this book is essentially a mixture of environmental science (Chapters 1 and 2) and chemical reactor engineering (Chapters 3 to 6). The question is why environmental issues are being presented in a chemical engineering book containing heterogeneous processes. First of all, the processes discussed (adsorption, ion exchange, and catalysis) are largely employed in our effort to minimize the emissions of various pollutants into the environment. The use of catalytic converters in the after-treatment of automobile exhaust gases (catalysis), a subject known to almost everyone, the treatment of VOC-containing gases (catalysis, adsorption), and the removal of toxic metals from industrial wastewater (ion exchange, adsorption) are some important examples of the environmental applications of the presented heterogeneous processes. Moreover, the environment is a field of continuous research since its protection is essential in the context of sustainable development, and generally for a planet worth living on. Consequently, the environmentally oriented presentation of adsorption, ion exchange, and catalysis is of substantial importance. Engineers are often provided with a lot of information regarding heterogeneous processes, chemical phenomena, and waste treatment techniques, but lack know-ledge of the very basics of environmental problems. The first two chapters of the book do not attempt to cover all environmental subjects; they are an effort of the authors to provide the reader with a basic knowledge of the major environmental problems and connect them to chemical engineering. Why adsorption, ion exchange and heterogeneous catalysis in one book? The basic similarity between these phenomena is that they all are heterogeneous fluid–solid operations. Second, they are all driven by diffusion in the solid phase. Thus, mass transfer and solidphase diffusion, rate-limiting steps, and other related phenomena are common. Third, the many aspects of the operations design of some reactors are essentially the same or at least similar, for example, the hydraulic analysis and scale-up. Furthermore, they all have important environmental applications, and more specifically they are all applied in gas and/or water treatment. In connection with the engineering content of the book, a large number of reactors is analyzed: two- and three-phase (slurry) agitated reactors (batch and continuous flow), twoand three-phase fixed beds (fixed beds, trickle beds, and packed bubble beds), three-phase (slurry) bubble columns, and two-phase fluidized beds. All these reactors are applicable to catalysis; two-phase fixed and fluidized beds and agitated tank reactors concern adsorption and ion exchange as well. Apart from the analysis of kinetics, mass transfer, and equilibrium of the processes at a fundamental level, the analysis of material, and in fixed beds energy balances in the reactors, as well as a number of analytical solutions of the reactors models are presented. Furthermore, the hydraulic behavior of the reactors is presented in detail. Hydraulic analysis is basically vii

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flow analysis of the reactors or conducting patterns without taking into consideration the physicochemical phenomena involved, i.e. adsorption, ion exchange, or catalysis. Hydraulic analysis follows different routes for each reactor type. On the basis of the reactor characteristics, the most important aspects are analyzed: nonideal flow and liquid holdup for fixed beds, bubble analysis and phases holdup for slurry bubble columns and agitated tanks, fluidizing regimes and analysis for fluidized beds, and agitation analysis for tank reactors. Furthermore, scale-up analysis is included for all the reactors. Special reference should be made for the last section of Chapter 3: Particle analysis. Everything in connection with particle properties and basic calculations, irrespective of its specific use, is presented: from particle surface area to calculations regarding its terminal velocity and diffusion coefficients. Furthermore, concerning materials used in adsorption, ion exchange, and catalysis, special paragraphs are included in Chapters 4 and 5 as well as the management of spent materials. Nearly all the examples contained are based on real experimental data found in the literature with environmental interest. Most of the examples consider all aspects of operation design—kinetics, hydraulics, and mass transfer. All parameters in the examples are calculated using correlations, figures, and tables provided in the book—thus no parameters just “appear” in the text. Moreover, some text in the examples is also devoted to provide information about the pollutants removed or treated. Special issues are found in “Look into” paragraphs. These sections could be viewed as highlights. Here, special subjects are discussed in detail. Special cases, where significant applications are presented, can be also be found in the book. Finally, Appendices I and II are structured so that the reader can easily find some basic properties of environment-related compounds and unit conversions, or smartly estimate some parameters found in relationships in the rest of the book. The Appendix does not cover all cases and compounds; however, it is a first and separate guide provided to the reader for the basic calculations and properties found in this book.

Table of Contents 1. Air and Water Pollution

2. Adsorption, Ion Exchange and Catalysis

3. Heterogeneous Processes and Reactor Analysis

4. Adsorption and Ion Exchange

5. Catalysis

6. Reactors Scale up

7. Appendices

8. References

–1– Air and Water Pollution

1.1 INTRODUCTION The reason for the presence of this chapter in this book is more than apparent to its authors. In the last century, human activities in combination with a lack of respect for nature, expressed by the view that raw material resources are inexhaustible and the tolerance of ecosystems to pollutant release is unlimited, led to the pollution and degradation of the environment. Chemical engineering is the field of science that combines chemistry with technology and is able to give solutions to most environmental problems. The environment is going to set the pace in chemical engineering evolution, since we have reached the point where if we do not stop destroying and polluting the environment, life on Earth will be in danger—at least life as we know it today. Sustainability is the proposal for a better future, where economic development can coexist with social cohesion and environmental protection. Human activities harmfully influence the environment and nature in many ways. The production of undesirable wastewater, waste gas, and liquid plus solid residues seems to be inevitable during chemical processes. The public is more sensitive to pollution of the aquatic environment and the depletion of clean water resources, because they have an immediate impact on daily routine and recreational activities. However, air pollution has an adverse impact on our health in the short and long term, and the problems of the greenhouse effect and the destruction of stratospheric ozone could extinguish life from the face of the Earth. These problems are enhanced by overpopulation and urbanization. Today, urban areas can be seen as “monsters” that consume large amounts of energy, matter, and freshwater and release all kinds of waste into the environment. The first chapter of this book is devoted to air and water pollution. Although much attention will be given to releases from chemical processes, environmental problems arising from other activities will also be dealt with. Issues concerning pollutants, emission sources, and treatment methods are going to be presented. Moreover, before focusing on the processes of adsorption, ion exchange, catalysis, and the design principles of the relevant operations in the following chapters, it is useful to show their connection with environmental protection. Emphasis is given on the current environmental situation in Europe.

1

2

1. Air and Water Pollution

1.1.1 Air pollution Clean air is an important prerequisite for sustainable development and is a basic requirement for human health and welfare. In addition, air pollutants contribute to atmospheric problems such as acidification and global climate change, which have impacts on crop productivity, forest growth, biodiversity, buildings, and cultural monuments. The benefits from the progress made in the areas of waste gas treatment and environmental legislation are partially offset by industrialization, an increase in the number of private cars in use, and overpopulation. Air pollutants are divided into two broad categories: primary and secondary. Primary pollutants are those emitted directly into the air, in contrast to secondary pollutants, which are created in the atmosphere by the reactions among the primary pollutants, usually in the presence of sunlight. Specifically, a variety of chemical or photochemical reactions (catalyzed by light) produce a wide range of secondary pollutants, especially in urban air. A prime example is the formation of ozone in smog. There is a variety of problems associated with air pollution, starting from photochemical smog, ozone formation, and acid rain at a regional level, to the greenhouse effect and ozone-layer depletion at a global level. These problems have an adverse impact on both environment and public health (Table 1.1); the last two problems are a threat to life on Earth generally. Agriculture, energy plants, road transport, and industry are the most important sources of pollutants of the atmosphere. Agriculture, for example, charges air with acidifying gases that may lead to acid rain formation with a dramatic impact on lakes, rivers, and marine life. Air pollution is a problem at a local as well as a global level. For purposes of studying, it is useful to catagorize air pollution according to the levels at which it appears: Table 1.1 The main health effects of the most important air pollutants (Source: Parliamentary Office of Science and Technology, 2002; UNEP, 1992) Pollutant

Main Health Effects

Sulfur dioxide

Irritation of lungs, shortness of breath, increased susceptibility to infection Irritation or damage of lungs Eye and nasal irritation, long-term exposure associated with coronary heart disease and lung cancer Interferes with oxygen transport by blood, resulting in the reduction of oxygen supply to the heart (chronic anoxia), heart and brain damage, impaired perception Pain on deep breathing, irritation and inflammation of lungs, heart stress or failure Cause of cancer Cause of cancer Kidney disease and neurological impairments, primarily affecting children

Nitrogen dioxide Particulate matter Carbon monoxide

Ozone and other photochemical oxidants Benzene 1,3-Butadiene Lead

1.1 Introduction

• • • • •

3

local urban regional transboundary global.

Local level At the local level, air pollution concerns a region within a 5-km radius. It is characterized by high concentrations of specific pollutants that may come from automobiles or industrial activities in that region. For example, emissions from vehicles can lead to high concentrations of carbon monoxide near traffic-jammed roads. High buildings and the terrain can also contribute to high local concentrations of pollutants. Urban areas In urban areas, there are three major types of air pollution found (EEA, 2003): •

•

•

High annual average concentration levels of various pollutants, e.g. benzene, lead, sulfur dioxide (SO2), and particulate matter (PM). As in the case of air pollution at the local level, this type of pollution is linked to specific pollutants resulting from either large industrial and power plants or automotive vehicles. Winter-type smog, characterized by high concentrations of SO2 and PM that arise mainly from the combustion of coal and fuels with a high content of sulfur. This kind of pollution occurs in urban areas with many power plants or industrial units clustered together, where low temperatures and mist are observed in the year. It has been also termed “industrial pollution.” Summer-type smog, characterized by high concentrations of carbon monoxide, volatile organic compounds (VOCs), and nitrogen oxides (NOx). It is also called “photochemical smog” or “LA smog”, since it first appeared in Los Angeles. This type of pollution is closely connected to automotive vehicles, and its formation is favored by sunlight and high temperatures.

The first two types are associated with primary pollutants, namely, compounds such as nitrogen oxide (NO), carbon monoxide (CO), and sulfur dioxide (SO2) that are emitted directly into the atmosphere from various sources, whereas the third type of urban air pollution is a more complex phenomenon associated with secondary pollutants, which are formed from reactions between primary pollutants in the atmosphere, usually in the presence of sunlight and heat. Specifically, various volatile organic compounds react in the atmosphere with nitrogen oxides by means of the ultraviolet radiation of sunlight, producing the so-called photochemical smog consisting of nitrogen dioxide, various oxidized forms of organic compounds, and ozone. The pollution of primary pollutants is easier to treat than the one associated with secondary pollutants, because the latter are produced by various organic compounds participating in numerous photochemical reactions. Approximately half of the world’s population now lives in urban areas, and half of these people suffer an atmosphere containing harmful amounts of substances such as sulfur dioxide, ozone, and particulate matter. Approximately 4000 people died from lung and heart conditions during a London smog episode in December 1952. Globally, around 50% of cases of chronic respiratory illness are now connected with air pollution. The most

4


recent information on the health effects of air pollution have been summarized in a report released by the World Health Organization (WHO, 2004). Regional Regional air pollution (50–1000 km) can be attributed to two mechanisms: • •

transport and dispersion of urban pollutants in large areas, transport and transformation of primary pollutants into secondary ones at the regional level.

Two types of air pollution are connected to these mechanisms. The first problem is the formation of tropospheric ozone due to the action of sunlight on mixtures of NOx and VOCs. Tropospheric ozone is either formed in urban areas and transferred by the wind to rural areas or formed during the transfer of NOx and VOCs from cities to rural areas by the wind and has adverse effects on crop yields and human health as well as forest ecosystems (ApSimon and Warren, 1996). About 90% of the adverse impact of air pollution on agriculture is attributed to ozone. The second problem is associated with acidifying substances, such as SO2, NO, and NO2, which are photooxidized in the atmosphere and taken up by cloud droplets, being thus transformed into the corresponding acids, causing acidification through acid rain formation or dry deposition (direct contact of acids with land and aquatic surfaces by means of wind), which constitutes an immediate danger for land and aquatic biota (Figure 1.1). Transboundary Transboundary air pollution is related to the transfer of air pollution from one country to another. Generally, the problems that appear on a regional level may also be exchanged

Figure 1.1

The mechanism of acid deposition (ApSimon and Warren, 1996).

1.1 Introduction

5

between adjacent countries. For example, Greece exchanges ozone with Italy, whereas Japan and Canada “import” various types of air pollution from China and the United States, respectively. Moreover, the transboundary transport of acidifying pollutants has led to a marked change in the acidity of lakes and streams in Scandinavia, as observed in the 1960s (ApSimon and Warren, 1996). These problems have also been observed to appear on an intercontinental scale. For example, based on observations of atmospheric carbon monoxide, ozone, peroxyacetyl nitrate, radon, aerosols, and nonmethane hydrocarbons (NMVOCs), Jaffe et al. (1999) showed that emissions from East Asia significantly impact the air arriving to North America. North America has also been reported to “export” air pollutants to Europe (Holloway et al., 2003). A number of toxic substances are capable of intercontinental atmospheric transport, including mercury, toxaphene, hexachlorobenzene, and poly-chlorinated biphenyls (PCBs) (International Air Quality Advisory Board, 2004). This type of air pollution is another proof of the fact that pollution is a problem that needs intergovernmental cooperation to be properly addressed. Global Global air pollution is related to two famous problems that are responsible to a great extent for the increased public awareness about environmental issues. The greenhouse effect and stratospheric ozone depletion changed our view and attitude about the environment. For the first time, it was realized that our activities could lead to a drastic global change in the climate or atmosphere. The extensive use of chlorofluorocarbons (CFCs) till 1985 resulted in a decrease in the ozone concentration in the stratosphere, which led to an increase in the amount of UV-B radiation reaching the Earth’s surface. Ozone depletion has many adverse effects on both ecosystems and humans, independent of geographical position or economic status. As a result, nonmelanoma skin cancers have increased, leading to concerns about human health, and the photosynthesis process, vital for life support on the Earth, has been put in danger (UNEP, 1992). Although Molina and Rowland reported the possibility of ozone destruction by the action of CFCs in 1974, it was the discovery of the ozone “hole” in the Antarctic stratosphere in 1984 that led to urgent and intergovernmental action (Farman et al., 1985). The rapid evolvement of ozone depletion and its obvious tremendous potential effects led to the decision to ban and replace all CFCs. At the same time, the emissions of some compounds into the atmosphere, mainly carbon dioxide (CO2), seem to cause global warming. The word “seem” is used because the effect of increased levels of CO2 in the atmosphere has not been fully understood. In contrast to the issue of ozone depletion, the greenhouse effect is still under consideration and its potential impact on the Earth has not been fully concluded. In Table 1.2, air pollution problems are connected to the most important pollutants. At this point, it would be interesting to examine the situation in terms of air quality around the world. European Union The evolution of the emissions of some atmospheric pollutants in Europe (EU-15) in the period 1990–1999 has been presented in the report of Goodwin and Mareckova (2002). The report includes acidifying pollutants (ammonia, sulfur dioxide, and nitrogen oxides), tropospheric ozone precursors, NMVOCs, carbon monoxide, and particulate matter

6


Table 1.2 Air pollution problems in association with the most important pollutants Pollutant

Smog

Urban air quality

Acid deposition

Global warming

Ozone Sulfur dioxide Carbon monoxide Carbon dioxide CFCs Nitrogen oxides Volatile organic compounds Toxicsa Particles Total reduced sulfur compounds

a

Ozone depletion

Health

Toxic metals and organic compounds.

Table 1.3 The most significant sources of atmospheric pollutants in Europe in 1999 Pollutant

Emission Sources

CO NMVOC

Road transport (57%), industry (16%), other transport (7%) Road transport (31%), solvent and other product use (32%), industry (10%), agriculture (7%), energy (6%) Transport (64%), energy sector (16%), industry (13%) Agriculture (94%) Energy sector (61%), industry (24%), commercial and domestic combustion (7%), transport (7%) Road transport (28%), energy industries (24%), industry (16%), agriculture (13%)

NOx NH3 SO2 PM

emissions. Between 1990 and 1999, the emissions of these pollutants have been significantly reduced (SO2 by 70%, NOx by 25%, NMVOCs by 28%, and CO by 32%), mainly due to the introduction of automobile catalytic converters with the exception of ammonia, whose emissions due to agriculture are very difficult to control. The energy sector, and specifically its combustion processes, plus road transport are the main sources of air pollutants except ammonia (NH3). The most significant sources of some pollutants in EU-15, in 1999, are shown in Table 1.3.

1.1 Introduction

7

Despite the benefits from stringent legislation and advances in environmental technology, the increase in the fleet of automotive vehicles and overpopulation in urban areas results in bad air quality. It is estimated that up to 45% of Europe’s urban population remains exposed to particulate concentrations above limit values, and up to 30% to ozone concentrations above target levels that assure human health protection. The concentrations of various pollutants in the atmosphere in various cities across Europe are shown in Table 1.4. The data in the table are from WHO’s Healthy Cities Air Management Information System and the World Resources Institute, which relies on various national Table 1.4 The concentrations of various pollutants in the atmosphere in various cities in Europe, in 1995 Country

City

City population (⫻1000)

Total suspended particulates (g/m3)

Sulfur dioxide (g/m3)

Nitrogen dioxide (g/m3)

Austria Belgium Bulgaria Croatia Czech Republic Denmark Finland France Germany

Vienna Brussels Sofia Zagreb Prague Copenhagen Helsinki Paris Frankfurt Berlin Munich Athens Budapest Reykjavik Dublin Milan Rome Turin Amsterdam Oslo Katowice Warsaw Lodz Lisbon Bucharest Bratislava Madrid Barcelona Stockholm Zurich London Manchester Birmingham

2060 1122 1188 981 1225 1326 1059 9523 3606 3317 2238 3093 2017 100 911 4251 2931 1294 1108 477 3552 2219 1063 1863 2100 651 4072 2819 1545 897 7640 2434 2271

47 78 195 71 59 61 40 14 36 50 45 178 63 24

14 20 39 31 32 7 4 14 11 18 8 34 39 5 20 31 – – 10 8 83 16 21 8 10 21 11 11 5 11 25 26 9

42 48 122 – 23 54 35 57 45 26 53 64 51 42 – 248 – – 58 43 79 32 43 52 71 27 25 43 29 39 77 49 45

Greece Hungary Iceland Ireland Italy Netherlands Norway Poland Portugal Romania Slovak Republic Spain Sweden Switzerland United Kingdom

77 73 151 40 15 .. .. .. 61 82 62 42 117 9 31 – – –

8


sources. For reasons of comparison, the population of each city is also presented. Total suspended particulates refer to smoke, soot, dust, and liquid droplets from combustion that are in the air. It has to be noted that particulate levels are an indicator of the quality of the air and the state of a country’s technology and pollution controls. Although pollutant concentrations are sensitive to local conditions, and therefore, the data presented should be considered as a general indication of air quality in each city, some comparisons can be conducted. WHO annual mean guidelines for air quality standards are 90 g/m3 for total suspended particulates, 50 g/m3 for sulfur dioxide, and 50 g/m3 for nitrogen dioxide. Sofia, Athens, Turin, and Barcelona, all in south Europe, exhibit the highest concentrations of total suspended particulates, above the WHO standard value. Concerning sulfur dioxide, all the cities presented exhibit concentration values below the standard set by WHO. In contrast, nitrogen dioxide is a cause for concern in many European cities, since values close to or above WHO standards are common. For this compound, cities in northern Europe also have to be on alert. The combination of Tables 1.2 and 1.3 reveals the responsibility for each kind of air pollution. Specifically, road transport is the main source of nitrogen dioxide, whereas the increased sulfur dioxide levels should be attributed to the energy sector. Both sources contribute to increased levels of total suspended particulates in the atmosphere. It is obvious that this type of information is a valuable asset for environmental policy makers. Measurements in relation to air quality and exhaust emissions from automotive vehicles have been conducted for many years, and as a result, the evolution of air quality and the contribution of vehicles are known to authorities. However, this is not the case for industry. Even if the contribution of industry to air pollution could be roughly estimated, it was very difficult to connect each kind of industrial process to each pollutant. This lack of information led to the idea of establishing a Pollutant Release and Transfer Register (PRTR), which first emerged in the United States following the tragic accident in Bhopal, India, in 1984. Shortly thereafter, the United States Congress approved the Emergency Planning and Community Right-to-Know Act, establishing a register called the Toxic Release Inventory (TRI), which tracks releases to all media (air, water, and land) and offsite transfers of more than 600 chemicals. A look into the origins of U.S. TRI In 1984, a deadly cloud of methyl isocyanate killed thousands of people in Bhopal, India. Shortly thereafter, there was a serious chemical release at a sister plant in West Virginia. These incidents underscored demands by industrial workers and communities in several states for information on hazardous materials. Public interest and environmental organizations around the country accelerated demands for information on toxic chemicals being released. Against this background, the Emergency Planning and Community Right-to-Know Act (EPCRA) was enacted in 1986. EPCRA’s primary purpose is to inform communities and citizens of chemical hazards in their areas. Sections 311 and 312 of EPCRA require businesses to report the locations and quantities of chemicals stored on-site to state and local governments in order to help communities prepare to respond to chemical spills and similar emergencies. EPCRA

1.1 Introduction

9

Section 313 requires the EPA and the states to annually collect data on releases and transfers of certain toxic chemicals from industrial facilities, and make the data available to the public in the Toxics Release Inventory (TRI). Other countries, including Australia (NPI in 1998), Canada (NPRI in 1999), and the European Union (EPER in 2000), followed in developing national PRTR systems. Focusing on Europe, it is important to clarify what EPER is about, exactly. The European Pollutant Emission Register (EPER) is the establishment of a European register with comparable data on the emissions from individual activities covered by the Council Directive 96/61/EC, known as the IPPC Directive. It is a useful monitoring tool and a suitable instrument for public dissemination of emission data and its effectiveness could be seen in the European effort to achieve the goals set in Agenda 21 of the UNECE Conference in Rio de Janeiro (1992), in the IPPC Directive (1996), and in the UNECE Aarhus Convention (1998). The IPPC Directive was brought into effect at the end of 1999. Since then, member states have gradually adopted national regulations to comply with the IPPC Directive, including a national inventory of emission data to be reported to the commission. An inventory of principal emissions and sources responsible has to be published by the commission every three years based on data supplied by the member states. On January 25, 2000, the committee referred to in Article 19 of IPPC Directive gave a favorable opinion of a draft Commission Decision on the implementation of a European Pollutant Emission Register. The Commission Decision (2000/479/EC), to be referred to as the EPER Decision, was adopted on July 17, 2000. According to the EPER Decision, member states shall report to the Commission on emissions into air and water from all individual facilities with one or more activities as mentioned in Annex I to the IPPC Directive. The provided data will be made publicly accessible and disseminated on the Internet. Specifically, “EPER is a publicly accessible register with emission data that enables the Commission and national governments to monitor the trends in annual emissions of large industrial activities covered by Annex I of the IPPC Directive” (Commission Decision, 2000). EPER covers the releases into the environment from industrial facilities above a minimum production capacity. It excludes emissions from the transport sector and from most agricultural sources. The comparison with the EU-15’s total emissions of some important greenhouse gases and air pollutants (as reported under the UN Framework Convention on Climate Change and the UNECE Convention on Long-Range Transboundary Air Pollution) shows that EPER covers around— • • • • • •

42% of EU-15’s total—carbon dioxide (CO2) emissions 15% of EU-15’s total—methane (CH4) emissions 13% of EU-15’s total—dinitrogen oxide (N2O) emissions 6% of EU-15’s total—nonmethane volatile organic carbon (NMVOC) emissions 26% of EU-15’s total—nitrogen oxides (NOx) emissions , and 70% of EU-15’s total—sulfur oxides (SOx) emissions.

There are still some problems about the enforcement of the relevant directives and guidelines, and at present, data are available for 2001 only. However, it should be seen as a huge

10


step in environmental policy, since the knowledge of the connection between facilities and pollutants combined with the data about other sources of pollution (transport and agriculture) allow authorities for the first time to take the right course of action for each type of pollution. It has to be mentioned again that all releases into the environment are included in EPER. Suppose that the authorities have to deal with the problem of photochemical smog. This type of pollution is connected to increased NOx levels in the atmosphere. It is well known that road transport is the main source of these compounds. But the contribution of industry is still missing. The existence of the EPER and the corresponding National Pollutant Release and Transfer Register has enabled the collection of the data in Table 1.5. Consequently, the Table 1.5 NOx releases to air for each activity in Europe (2001) Activity

Total emission to air (t/yr)

Percentage

Combustion installations⬎50 MW Installations for the production of cement klinker (⬎500 t/d), lime (⬎50 t/d), glass (⬎20 t/d), mineral substances (⬎20 t/d), or ceramic products (⬎75 t/d) Metal industry Mineral oil and gas refineries Basic organic chemicals Industrial plants for pulp from timber or other fibrous materials and paper or board production (⬎20 t/d) Installations for the disposal or recovery of hazardous waste (⬎10 t/d) or municipal waste (⬎3 t/h) Basic inorganic chemicals or fertilizers Slaughterhouses (⬎50 t/d), plants for the production of milk (⬎200 t/d), other animal raw materials (⬎75 t/d), or vegetable raw materials (⬎300 t/d) Installations for the disposal of nonhazardous waste (⬎50 t/d) and landfills (⬎10 t/d) Coke ovens Pharmaceutical products Installations for surface treatment or products using organic solvents (⬎200 t/yr) Coal gasification and liquefaction plants Biocides and explosives Plants for the pretreatment of fibers or textiles (⬎10 t/d) Installations for the disposal or recycling of animal carcasses and animal waste (⬎10 t/d) Installations for the production of carbon or graphite Total

1.5 ⫻ 106 427 ⫻ 103

58.3 16.9

186 ⫻ 103 177 ⫻ 103 72 ⫻ 103 67 ⫻ 103

7.3 7.0 2.8 2.7

43 ⫻ 103

1.7

42 ⫻ 103 14 ⫻ 103

1.7 0.6

6.8 ⫻ 103

0.3

5.9 ⫻ 103 5.2 ⫻ 103 3.5 ⫻ 103

0.2 0.2 0.1

1.9 ⫻ 103 1.1 ⫻ 103 941

0.1 0.0 0.0

663

0.0

441

0.0

2.5 ⫻ 106

100.0

1.1 Introduction

11

measures that will be taken have to be focused not only on road transport but also on combustion installations. A special case: Suppose that your country has occasions of acid rain due to sulfuric acid. The main source of SOx, which leads to sulfuric acid formation, is combustion installations. However, there are not large combustion installations in your land. Can you find the source of air pollution and the possible position of your country in Europe, using Table 1.6? The answer is simple. You have been the victim of air pollution “traveling”. Acid rain is among the problems connected to air pollution that may appear at a transboundary level. So, your country has suffered the results of the combination of the elevated SOx emissions from a neighboring country with favoring climatic conditions. Air pollution is not a problem only in Europe but constitutes a reason to worry all over the world. The concentrations of total suspended particulates, sulfur dioxide, and nitrogen dioxide in the atmosphere in various cities in 1995, in America, Asia, Africa, and Australia are presented in Tables 1.7, 1.8, 1.9, and 1.10, respectively. It is useful to recollect the limits set by WHO: 90 g/m3 for total suspended particulates, 50 g/m3 for sulfur dioxide, and 50 g/m3 for nitrogen dioxide. It is apparent that the world has a long way to go till compliance with these numbers is achieved. Mexico City is notorious for bad air quality. Pollution levels exceed WHO standards 350 days per year. More than half of all children in the city have lead levels in their blood sufficient to lower intelligence and retard development. The 130,000 industries and 2.5 million motor vehicles spew out more than 5500 metric tons of air pollutants every day, which are trapped by the mountains ringing the city. Table 1.6 SOx releases (annual) to air for each country in Europe (2001) Country

Total emission (t)

Percentage of European total

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Total

12,321 105,539 12,433 59,436 369,051 370,590 408,222 91,498 509,126 604 51,777 166,147 1,169,999 23,403 948,488 4,298,634

0.3 2.5 0.3 1.4 8.6 8.6 9.5 2.1 11.8 0.0 1.2 3.9 27.2 0.5 22.1 100.0

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Table 1.7 The concentrations of various pollutants in the atmosphere in various cities in America, in 1995 Country

City





Argentina Brazil

Córdoba City São Paulo Rio de Janeiro Toronto Montreal Vancouver Santiago Bogotá Havana Guayaquil Quito Mexico City New York Los Angeles Chicago Caracas

1294 16,533 10,181 4319 3320 1823 4891 6079 2,241 1831 1298 16,562 16,332 12,410 6844 3007

97 86 139 36 34 29 – 120 – 127 175 279 – – – 53

– 43 129 17 10 14 29 – 1 15 31 74 26 9 14 33

97 83 – 43 42 37 81 – 5 – – 130 79 74 57 57

Canada Chile Colombia Cuba Ecuador Mexico United States Venezuela

Most of the Third World megacities (those with populations greater than 10 million people) experience similar problems. Air quality in Cairo, Bangkok, Jakarta, Bombay, Calcutta, New Delhi, Shanghai, Beijing, São Paulo, and many lesser known urban areas regularly reach dangerous levels. In the following sections, the most important problems of the atmospheric environment across the Earth are presented briefly. North America Electric power plants are the major source of toxic air pollutants in North America, accounting for almost half of all industrial air emissions in 2001 (CEC, 2004). According to the data provided by industrial facilities, 46 of the top 50 air polluters in North America were power plants. The sector is responsible for the 45% of the 755,502 t of toxic air releases in 2001, with hydrochloric and sulfuric acids being most commonly released from the burning of coal and oil. Power plants also accounted for 64% of all mercury emissions to the air. However, air quality in Canada and the United States shows the clearest trend of improvement among all environmental categories during the last two decades. The reports predicting that there would be a sharp decline in air quality after the signing of the North America Free Trade Agreement (NAFTA) were incorrect. For example, The Environmental Implications of Trade Agreements, released by the Ontario Ministry of Environment and Energy in 1993, predicted that pollutants such as sulfur dioxide would increase by more than 4.5% annually in North America as a direct result of NAFTA. However, data from Environment Canada, the United States, and the Organization for Economic Cooperation and Development (OECD) show that sulfur dioxide levels in North

1.1 Introduction

13

Table 1.8 The concentrations of various pollutants in the atmosphere in various cities in Asia, in 1995 Country

City





China

Shanghai Beijing Tianjin Bombay Calcutta Delhi Jakarta Tehran

13,584 11,299 9415 15,138 11,923 9948 8621 6836

246 377 306 240 375 415 271 248

53 90 82 33 49 24 – 209

73 122 50 39 34 41 – –

Tokyo Osaka Yokohama Seoul Pusan Taegu Kuala Lumpur Manila Moscow

26,959 10,609 3178 11,609 4082 2432 1238 9286 9269

49 43 – 84 94 72 85 200 100

18 19 100 44 60 81 24 33 109

68 63 13 60 51 62 – – –

1199 2848 6547 7911 2826 2031 2809

100 – 223 – 57 – 100

9 20 11 120 55 – 14

30 30 23 – 46 – 51

India Indonesia Iran, Islamic Rep. Japan Korea, Rep. Malaysia Philippines Russian Federation Singapore Thailand Turkey Ukraine

Omsk Singapore Bangkok Istanbul Ankara Izmir Kiev

Table 1.9 The concentrations of various pollutants in the atmosphere in various cities in Africa, in 1995 Country

City





Egypt, Arab Rep. Ghana Kenya South Africa

Cairo Accra Nairobi Capetown Johannesburg Durban

9690 1673 1810 2671 1849 1149

– 137 69 – – –

69 – – 21 19 31

– – – 72 31 –

14


Table 1.10 The concentrations of various pollutants in the atmosphere in various cities in Australia and New Zealand, in 1995 Country

City





Australia

Sydney Melbourne Perth Auckland

3590 3094 1220 945

54 35 45 26

28 0 5 3

– 30 19 20

New Zealand

America are continuing to fall. These countries are an excellent proof of the fact that economic growth can keep up with environmental protection through appropriate legislation and application of clean technologies. Latin America Air pollution in Latin America is a great problem in the major cities of this region, i.e. Mexico City (Mexico), Rio de Janeiro (Brazil), São Paulo (Brazil), and Buenos Aires (Argentina). Mexico City and Santiago (Chile) are among the most polluted cities in the world. Almost 70% of the population lives in urban areas and suffers high concentration levels of various pollutants. The major causes of air pollution are population growth, industrialization, and increased vehicle use. The situation is so severe that the health effects associated with air pollution are evident. In 1992, it was estimated that over 27 million people in Latin America were constantly exposed to high levels of particulate air pollution, which led to approximately 4000 cases of premature mortality each year. Indeed, the air is so polluted in these cities that they are slowly becoming uninhabitable. Despite the measures taken (introduction of unleaded gasoline, limitation of private car use), urban air pollution continues to have a severe effect on the people. If drastic measures are not taken, the day will not be too long before we see the first environmental migrants. Asia Half the world’s population lives in urban areas in Asia. Although several attempts have been made to address air pollution in Asia, much progress needs to be made to deal with the issue of urban air quality in Asian megacities (cities with population over 10 millions). Urban air pollution in Beijing, Delhi, and Jakarta has worsened because of population growth, increased industrialization, and automobiles. The impact on public health is considerable; approximately 20–30% of all respiratory diseases are associated with air pollution. Specifically, rapid urbanization, with the associated growth in industry and transportation systems, has increased regional concerns with regard to emissions of sulfur dioxide and nitrogen oxides. According to estimations for the year 2000, sulfur dioxide emissions in Asia surpassed the emissions of North America and Europe combined. The primary manmade source of sulfur and nitrogen in the Asia-Pacific region is fossil fuel combustion in

1.1 Introduction

15

the energy, industry, and transportation sectors. The quality of air has deteriorated in virtually every Asian city, except for those in the Republic of Korea. The pollutant levels in these cities exceed WHO guidelines. Acid rain is another important problem in Asia. An example is the damage caused to the historic Taj Mahal in India by the local foundries, which used to burn fuel. Similarly, in China, acid rain has damaged metal structures and concrete works in cities of Chongging and Guiyan, where SO2 emissions are high. Weather patterns in Asia facilitate transboundary pollution. The potential was evident in the recent Indonesian forest fires. The area affected by the air pollutants from the fire spread for more than 3200 km, east to west, covering six Asian countries and affecting around 70 million people. In the Malaysian state of Sarawak, the air pollution index hit record levels of 839 (levels of 300 are equivalent to smoking 80 cigarettes a day and are officially designated as “hazardous”). Africa The African continent is climatically diverse. Humid tropical conditions prevail in Western and Central Africa and in the Western Indian Ocean islands; most Southern African countries experience arid and semiarid conditions, while semideserts and deserts are features of Northern Africa. The region experiences a high degree of variability and uncertainty in climatic conditions. This continent is believed to be the most vulnerable region to the impacts of climate change. In comparison to other regions, African countries emit negligible amounts of air pollutants and anthropogenic greenhouse gases. For example, Africa contributes less than 3.5% of global emissions of CO2. Nevertheless, anthropogenic atmospheric pollution is a problem in Northern and Southern Africa, and in some large cities, mainly due to the lack of up-to-date technology. Currently, 38% of Africa’s population lives in urban areas, but this percentage rises continuously, with a projection to reach 54% by 2030. The increasing overpopulation leads to a rise in vehicle emissions and greater industrialization, which in turn result in air quality deterioration in the continent. In many countries, the use of leaded gasoline is still widespread and vehicle emission controls are simply nonexistent. Dust from the Sahara carrying pollutants has been detected in south Europe and even in the eastern part of the United States. South Africa, Nigeria, and Egypt are the largest emitters of greenhouse gases in Africa. Australia Air pollution problems in Australia are insignificant compared to cities such as Mexico City and Athens. Australia is not densely inhabited and, being surrounded by oceans, does not import pollution from other countries. Moreover, various initiatives have been taken for controlling the sources of air pollution. Specifically, the use of incinerators has been considerably reduced, backyard burning has been banned, and power stations have been located far from large cities. Regarding motor vehicles, the most significant air pollution threat in Australia, new cars have been equipped with catalytic converters in their exhaust systems since 1986. However, in many cities in Australia, the combination of typical coastal conditions (the major urban airsheds are located on coastal strips with mountain ranges nearby), a high sunlight flux, and a large fleet of motor vehicles along with traffic congestion will continue to give a finite number of occasions each year when photochemical smog or particulate matter will be formed. These conditions can be considerably exacerbated by the

16


occurrence of bushfires or hazard reduction burning in the vicinity of the cities. Hopefully, even then, the current standards are not actually expected to be exceeded (Australian Academy of Technological Sciences and Engineering, 1997). 1.1.2 Water pollution Water covers 71% of the planet’s surface, mainly in the form of salty water in the oceans. It is a vital substance for supporting life on Earth. For example, a tree contains 60% water, most animals are composed of about 65% water, while our bodies contain around 55% water (Tyler Miller, 1999). Everyone needs fresh water everyday to cover the daily demand in food, domestic use, etc. Fresh water is used in agriculture, construction, transport, the chemical industry, and numerous other activities of human beings. The use of abstracted water in Europe is presented in Figure 1.2. It has to be noted that in many regions on Earth, where the struggle for existence of population is continuous, it is the lack of fresh and clean water that limits the production of food. According to the United Nations, the first priority of poor countries, especially in Africa, should be not financial support or technological knowledge but clean water supply to the population. Unfortunately, despite the fact that most of the planet is covered by water, only a small amount of this water is available as fresh water. Almost 97.5% of the total is in oceans in the form of salty water and is not suitable for drinking, watering, or industrial use as is. The remaining 2.5% is fresh water. However, not even that small amount is easily accessible or exploited, because it is stored as ice on the poles and on mountaintops. Furthermore, a significant amount of the rest lies so deep in the ground that it is very difficult to extract. In Figure 1.3, the distribution of water on Earth is presented. According

Industry* 14% Energy sector 38%

Public water supply 18%

Agriculture 30%

*excluding cooling water

Figure 1.2

The use of abstracted water in Europe (Nixon et al., 2004).

1.1 Introduction

17

Total water (100%) Salt (97.5%)

Fresh (2.5%)

Ice (1.7%)

Liquid (0.8%) Ground (0.79%)

Lakes (0.008%)

Rivers (0.0002%)

Soil (0.001%)

Atmosphere (0.0009%)

Organisms (0.0001%)

Figure 1.3

Water on Earth (Nixon et al., 2004).

to the EC, less than 1% of the planet’s water is available for human consumption and more than 1.2 billion people in the world have no access to safe drinking water. Fresh water is purified and reallocated through the hydrological cycle in nature. Nowadays, this natural process is inadequate due to human activities, and specifically because of the thoughtless wasting of water and discharge of various pollutants into the aquatic environment. These activities of human beings threaten not only the fresh water supply but also marine life. Moreover, with an ever-increasing world population, the situation is expected to worsen in the near future, especially in densely populated or industrial areas. These areas consume large amounts of fresh water, and at the same time produce and release large amounts of wastewater into the environment. Water-quality deterioration can be attributed to water pollution or contamination. Water pollution is generally defined as any physical, chemical, or biological alteration in water quality that has a negative impact on living organisms. In the stricter sense, pollution can be defined as the transfer of any substance to the environment. However, there is a tolerance limit for each pollutant, since zero-level pollution is economically and technically unpractical. The most important kinds of water quality deterioration are the following. Thermal Pollution The discharge of warm wastewaters into a surface receiver may have many adverse effects on aquatic life. The increase in temperature results in a decrease in the oxygen concentration in water and the elimination of the most sensitive species. Temperature changes may also cause changes in the reproductive periods of fishes, growth of parasites and diseases, or even thermal shock to the animals found in the thermal plume. Biological Pollution by Oxygen-Demanding Wastes The release of oxygen-demanding wastes into water (mainly biodegradable organic compounds) results in the decrease in oxygen dissolved in water due to its consumption by the aquatic microorganisms that decay the organic pollutants. A minimum of 6 mg of oxygen per liter of water is essential to support aquatic life. A few species, like carp, can survive in low-oxygen waters. Each biodegradable waste is characterized by the

18


biological oxygen demand (BOD), which is a measure of the amount of dissolved oxygen needed by aquatic microorganisms for the degradation of waste. Pollution by Persistent Organic Chemicals (POPs) Besides biodegradable organic compounds, there are also organic substances that show great resistance and high lifespan in the environment, thus constituting a long-term danger to life. Dioxins, polychlorinated biphenyls (PCBs), and pesticides (DDT and others) are man-made compounds that remain intact for months in the environment. Consequently, people and animals at the top of the food chain eventually consume food containing these compounds. DDT, a popular compound that helped in the elimination of malaria, was proved to have many adverse effects on natural life. Paul Muller, who discovered the effectiveness of DDT as an insecticide in 1939 was awarded the Nobel Prize in medicine and physiology in 1948 for this discovery. Today, DDT is banned in most developed countries. Eutrophication by Nitrates and Phosphorus Eutrophication is the rapid depletion of dissolved oxygen in a body of water because of an increase in biological productivity. It is connected to the excess presence of plant nutrients in the environment, mainly nitrates and phosphorus. These compounds are connected to the excessive use or production of fertilizers. Inorganic Pollutants Metals, nonmetals, and acids/bases released by human activities severely deteriorate water quality, since they are toxic even at concentrations of parts per million. It has to be noted that heavy metals are extremely dangerous to human health and aquatic life. But what is worse is that there is nocycle of natural treatment of these substances. Inevitably, heavy metals remain intact in the environment and finally, they are accumulated in the food chain (bioaccumulation). Unless we take the right course of action, problems associated with both quality and quantity of water are going to be encountered, even in regions that seem to have sufficient amounts of clean water today. After the disputes and wars over the possession of oil in the past, water may be the next conflict territory between adjacent countries, even in Europe. According to the EC, 20% of all surface water in the European Union is seriously threatened with pollution. Furthermore, water is far from being evenly distributed in Europe and this is one major reason for resource problems. For example, whereas freshwater availability is more than 100,000 m3 per capita per year in Iceland, it is less than 10,000 m3 per capita per year in Switzerland, Portugal, and Spain. The available water in a country or region depends on the rainfall and on the net result of flows from and to its neighbors, mainly through rivers. For example, in Iceland, Norway, Sweden, Denmark, the United Kingdom, Italy, Spain, France, and Finland, more than 98% of freshwater is generated within the country, whereas in Hungary and the Netherlands more than 50% of freshwater is due to river flows from other countries (Nixon et al., 2004). So, most European countries rely more on surface water than groundwater. For example, Finland and Lithuania take more than 90% of their total supply from surface waters

1.1 Introduction

19

and Hungary, Poland, Germany, and Ireland, more than 80%. However, in many European countries, groundwater is the main source for public water supply. In Belgium, less than 40% of the total supply is from surface waters and in Denmark, less than 5%. We will look into the second leg of the water issue, namely the problems, sources, and pollutants associated with the pollution and contamination of the aquatic environment. Before moving on, it would be useful to examine the current situation in various regions of the world as concerns fresh water pollution (Kraemer et al., 2001). Europe In Europe, eutrophication is one of the main water pollution problems, which originates partially from the past European common agricultural policy. The intensive cultivation of land demanded the use of large amounts of fertilizers in a relatively small total land area. Although the situation has improved in the last few years with the phosphorus levels in water being decreased, the presence of nitrates in the aquatic environment is still a problem. Despite the fact that organic pollution still remains a problem, the steps taken to improve the situation cannot be overlooked. Specifically, the improvement in both wastewater treatment and emission controls has led to a significant decrease in the percentage of heavily polluted rivers, from 24% in the late 1970s to 6% in the 1990s in Western Europe. In contrast, the situation is not exactly the same in the southern member states, since 50% of the population is not yet connected to sewage treatment operations. Another problem in relation to aquatic receivers in northern and eastern Europe is acidification, whereas elevated concentrations of POPs are found near large European cities and industrialized areas. Eastern European countries involved in the accession process during the last few years have a lot to achieve in meeting the established water quality criteria set in EU legislation. In the Czech Republic and Slovakia, 57% of the drinking water in 1990 did not meet the quality criteria, whereas 70% of all water may be unacceptable for drinking in Poland. In the Russian Federation, industry, agriculture, and municipal landfills have contributed to the pollution of groundwater in 1400 areas. Moreover, high PCB concentrations have been detected in rivers and the levels of POPs draining into the Arctic may be higher than those found in urban America or Western Europe, in some cases. North America Agrochemical run-off is the main source of water pollution in the agricultural regions and has deteriorated the quality of groundwater in many areas. Over the past decade, however, there has been an improvement in the drinking water quality, especially in the United States, due to stricter water protection regulations in force. Nevertheless, industrialization and economic growth have resulted in new pollutants being introduced into water supplies. The case of the pollution of groundwater with MTBE (methyl tert-butyl ether) in many areas across the United States is characteristic. MTBE was used extensively in the 1990s as a gasoline additive instead of lead. Its properties (e.g. mobility in the water, low biodegradability) combined with its unsafe storage resulted in serious problems in the quality of drinking water, especially in the State of California.

20


Latin America In Latin America, 75% of the population lives in large cities, producing sewage volumes far higher than industrial ones. This high urbanization level combined with the fact that only 2% of domestic sewage is treated causes major problems to water supply and proliferation of pathogenic diseases. The gold mining industry is also a major polluter and is responsible for the presence of mercury in water. The extensive use of pesticides and fertilizers in agriculture poses another threat to water resources in Brazil, Colombia, and Ecuador. Japan Despite the strengthening of environmental policies, problems in relation to cadmium and cyanide, dioxin, organic pollution, lake eutrophication, residential sewage, pesticide runoff, industrial discharges, and groundwater contamination by toxics and nitrates also need to be tackled. Australia and New Zealand Nitrates and particularly phosphorus remain unacceptably high in Australia. Sedimentation constitutes a problem in both Australia and New Zealand, with the situation improving due to the removal of sheep from steep pastures. Sewage pollution has also been reduced because of the construction of sewage treatment plants. Asia The situation is more complex in the region of Asia and the Pacific. Water quality has many enemies there. First, sedimentation constitutes a major cause of pollution in Asian rivers, since sediment loads are four times the world average. Secondly, hazardous and toxic waste deteriorates the water quality. It is noteworthy that lead levels in Asia’s surface water are about 20 times higher than those in OECD countries. Thirdly, eutrophication is faced due to the extensive use of fertilizers in the last 30 years. But the list of problems does not end here. Asian rivers contain three times as many bacteria from human waste as the world average. Finally, urbanization and the release of untreated sewage and industrial waste to the environment are expected to cause severe water pollution problems. Water pollution trends have particularly worsened in China in recent years. The industrial development (as certified from oil demand) and the intensive agriculture (China is the world’s largest consumer of synthetic nitrogen fertilizers) pose a threat to water bodies. Severe water problems are also faced in southeast Asia. For example, Bangladesh and adjacent parts of India suffer from arsenic contamination of groundwater. Specifically, arsenic poisoning of groundwater has affected more than 50% of the total area of Bangladesh. The gradual introduction of arsenic inyo the food chain is more than a possibility. Nitrate pollution is also a problem in these areas. The discharge of raw and partially treated wastewater into the environment in the Mashriq subregion (Iraq, Jordan, Lebanon, Syrian Arab Republic, the West Bank, and Gaza) has deteriorated water quality and is a threat to public health. In the West Bank and Gaza, the list of problems in connection to groundwater includes high pesticide levels, and nitrate concentrations four times the WHO limit. In some central

1.2 Pollutants and Emission Sources

21

areas, 5 million m³ of drinking water is transported into the area every year, because groundwater is no longer potable.

1.2 POLLUTANTS AND EMISSION SOURCES Considerable information about pollutants, emission sources, and treatment techniques has been given in the reference document on best available techniques (BATs) in common wastewater and waste gas treatment released by the European Commission in 2003 (EC, 2003). 1.2.1 Air The sources of air emissions can be divided into two categories: • •

mobile sources, such as vehicles and ships stationary sources, such as chemical industries.

The exhaust air emissions are classified as • • •

ducted emissions—process emissions released through a pipe diffuse emissions—emissions that are not released via specific emission points (e.g. emissions during filling storage equipment, emissions from agriculture) fugitive emissions—emissions due to leaks

It is easily understood that whereas ducted emissions can be rather easily treated, the other two kinds of emissions can only be prevented or minimized. For example, agricultural emissions are very difficult to control. The main air pollutants are the following. • Carbon Dioxide Description: It is a nontoxic gas and the final product of complete combustion. Actually, it is the desirable and inevitable product of combustion. However, it is recognized as the main greenhouse gas, whose increased levels in the atmosphere play a large role in global warming. Source: Any combustion of fossil fuels. Combustion installations are responsible for 955 ⫻ 10 6 t/yr released into the air and 64.9% of the total emissions from industry in Europe. Impact: Global warming, climate change. • Sulfur Oxides and other sulfur compounds (H2S, CS2 , COS) Description: Sulfur dioxide (SO2) is a gas resulting from the combustion of coal, mainly in power plants, and certain types of liquid fuels that contain sulfur. In addition, it is produced during the manufacture of paper and smelting of metals. It may cause respiratory problems and permanent damage to the lungs when inhaled at high levels. It plays a major role in the production of acid rain (EPA site). Carbon disulfide (CS2) has many industrial

22


applications. It is released into the air from industries producing, using, or handling CS2, sanitary landfills, and natural-gas production and distribution. Source: Any combustion of sulfur-containing fuels. Combustion installations are responsible for 2.9 ⫻ 106 t/yr released into the air and 68.3% of the total emissions from industry in Europe. Impact: Winter-type smog, acidification. • Nitrogen Oxides (NOx , N2O) and other nitrogen compounds (NH3 , HCN) Description: Nitrogen oxides (NOx) are produced when fuel is burned at high temperatures. The main anthropogenic sources of NOx are motor vehicles, electric utilities, and other industrial, commercial, and residential sources that burn fuels. Nitrogen oxide (NO) is easily oxidized in the atmosphere to nitrogen dioxide, which reacts with volatile organic compounds in the atmosphere, thus contributing to photochemical smog. Nitrogen dioxide (NO2) can also react with hydroxyl radicals in the atmosphere forming nitric acid, a major component of acid rain. It can cause lung damage and illnesses of the respiratory system. Nitrous oxide (N2O) is one of the most drastic compounds in greenhouse effect. Moreover, it is stable for almost 120 years and can reach the stratosphere, where it participates in a reaction cycle catastrophic for ozone. Generally, nitrogen oxides may contribute to the greenhouse effect, acid rain, photochemical smog, and ground-level ozone formation. Source: Combustion of nitrogen-containing fuels (fuel NOx) or oxidation of atmospheric nitrogen during combustions at high temperatures. Transport is the main contributor, whereas in the industry sector, combustion installations are responsible for 1.5 ⫻ 106 t/yr released to air and 58.3% of the total emissions from industry in Europe. Impact: Global warming, acidification, photochemical smog, ozone layer depletion. • Incomplete combustion compounds, such as CO and CxHy Description: Carbon monoxide (CO) is a colorless, odorless, and poisonous gas. It is the product of any incomplete combustion of fossil fuels and many natural and synthetic products. After it is inhaled, it enters the blood through the lungs and reacts with hemoglobin, reducing the capacity of blood to carry oxygen to cells, tissues and organs. The body’s parts need oxygen for energy, so high-level exposures to carbon monoxide can cause serious health effects, with death possible from massive exposures. Symptoms of exposure to carbon monoxide can include vision problems, reduced alertness, and general reduction in mental and physical functions. Carbon monoxide exposures are especially harmful to people with heart, lung, and circulatory system diseases. It may cause chest pain, vision problems, difficulties in the learning ability of young children, and generally reduction in mental and physical functions. It also has a significant role in ozone production in the troposphere. Compounds in of the form CxHy are hydrocarbons, which may be unburned fuel compounds or produced by incomplete combustion. Their impact on environment and public health depends on the exact structure of the compound. Source: Road transport is the main source of carbon monoxide and unburned hydrocarbons in the atmosphere. Among industrial processes, the metal industry is responsible for 2.8 ⫻ 106 t/yr released into the air and 71.2% of the total emissions from industry in Europe. Impact: Health problems like chest pain and vision problems, photochemical smog.


23

• Volatile Organic Compounds (VOCs) and organosilicon compounds Description: Volatile organic compounds are released from burning fuels (gasoline, oil, wood coal, natural gas) and volatile liquid chemicals, such as solvents, paints, and glues. Automotive vehicles are also an important source of VOCs. The list of VOCs is lengthy and includes chemicals such as benzene, toluene, and methylene chloride. Depending on the specific compound, they can take part in ozone formation or may cause serious health problems such as cancer and other undesirable effects. Source: Road transport is the main source, whereas mineral oil and gas refineries are responsible for 0.2 ⫻ 106 t/yr released into the air, reaching 39.2% of the total industrial emissions. Impact: Depends on the exact compound; from health effects (cancer) to photochemical smog formation. • Particulate Matter (such as dust, soot, alkali, and heavy metals) Description: The sources of particulate matter are many: burning of wood, diesel, and other fuels, industrial plants, and agriculture. It leads to decrease in visibility in urban areas and poses a threat to health, since it enters the organism through the respiratory system. It has to be noted that compounds of low volatility that are formed secondarily in the atmosphere may be condensed on particulates, and as a result, the inhaled particles constitute a complex mixture of hazardous chemical compounds. The highest concentrations of airborne metal particles occur near mines, smelters, and metal processing/heavy engineering works. These particles are so small that they can be carried enormous distances by the wind. Mercury in particular, which largely occurs in gaseous form in the atmosphere, can be dispersed a very long way indeed. Source: Road transport and the energy sector. Combustion installations are responsible for 77 ⫻ 103 t/yr in the atmosphere, this being 47.9% of the total emissions from industry in Europe. Impact: Respiratory diseases, winter-type smog. It is easily concluded from the above that road transport and combustion installations are the main sources of air pollutants. In Table 1.11, the releases of the main air pollutants in connection to the main activities as covered in EPER are presented (transport is not covered). 1.2.2 Water The sources of water pollution are divided into • •

point sources, such as chemical industries and human communities nonpoint sources, such as agricultural activities and landfill leachates.

Point sources are mainly responsible for the pollution of surface waters (rivers, lakes, seas), whereas nonpoint sources mainly contribute to the pollution of groundwater resources. Moreover, releases from point sources can be treated by wastewater treatment plants, whereas nonpoint source releases can only be minimized.

24


Table 1.11 The releases of the main air pollutants from the industrial sector in Europe (2001) Compound

t/yr

Main source (% contribution)

Carbon dioxide Sulfur oxides Carbon monoxide Nitrogen oxides Methane

955 ⫻ 106 2.9 ⫻ 106 2.8 ⫻ 106 1.5 ⫻ 106 1.4 ⫻ 106

Volatile organic compounds Ammonia

0.2 ⫻ 106 85 ⫻ 103

Particulate matter Nitrous oxide

77 ⫻ 103 54 ⫻ 103

Fluorine Benzene Polyaromatic hydrocarbons

6.4 ⫻ 103 2 ⫻ 103 85.2

Mercury

7.4

Combustion installations (64.9%) Combustion installations (68.3%) Metal industry (71.2%) Combustion installations (58.3%) Installations for the disposal of nonhazardous waste and landfills (89.8%) Mineral oil and gas refineries (39.2%) Installations for poultry, pigs, or sows (76.6%) Combustion installations (47.9%) Basic inorganic chemicals or fertilizers (39.5%) Combustion installations (53.3%) Mineral oil and gas refineries (52.1%) Installations for surface treatment or products using organic solvents (32.9%) Combustion installations (31%)

The major amount of wastewater in the chemical industry does not come from chemical reaction steps, but from the subsequent physicochemical processing of the final reaction mixture. The most important pollutants of water are the following. • NH4⫹, NO3⫺, NO2⫺, PO43⫺ ions Description: These ions enter the environment in the form of salts. Their presence is due to the extensive use of fertilizers as a result of the intensification of agriculture. As they are plant nutrients, they can lead to eutrophication—the enrichment of water by nutrients, causing an accelerated growth of algae and higher forms of plant life, leading to an undesirable disturbance in the balance of organisms present in the water and in the quality of the water. Source: Agriculture is the main source, whereas the basic inorganic chemicals and fertilizer production activities are responsible for 29 and 25% of the total direct industrial releases of nitrogen and phosphorus, respectively, into water. Impact: Eutrophication, drinking water quality deterioration. • Heavy metals Description: Heavy metals are generally considered to be those whose density exceeds 5 g/cm3. Characteristic examples of heavy metals are cadmium (Cd), mercury (Hg), and lead (Pb) (Green et al., 2003). Cadmium is found in the environment due to general wastedisposal and industrial activities, including the mining industry, metal industry, coating/electroplating industry, production and deposition of batteries, burning of fossil fuels, the use of phosphate fertilizers, waste incineration, leaching from waste deposits, etc. Mercury is found in the environment due to its use in various products, e.g. batteries and


25

electronics. It can be found in four forms in the environment: methyl mercury, di- and monovalent ionic mercury, and metallic mercury. The main anthropogenic sources of lead in the environment are from general waste-disposal and industrial activities. Heavy metals can also be found naturally in the environment. Source: Industrial activities, waste disposal. The metal industry is responsible for 41.68 t Pb, 8.13 t Cd, 864.25 t Cr, 45.77 t Cu, 71.55 t Ni, and 5.09 t As releases directly into water, annually. Impact: Immediate threat to aquatic life and human population. • Inorganic salts and acids Description: Inorganic salts come from acid mine drainage, industrial processes, and drainage flow from irrigated areas. Salt accumulation on irrigated soils causes the most damage and loss in this category. A high proportion of sodium in irrigation water supply affects plant life adversely. Acids may come from industrial activities or from acid rain and cause lakes and streams to become acidic and unsuitable for many fish. Source: Mining industry, drain-off water. Impact: Acidification. • Oil compounds Description: Petroleum resulting from oil spills often pollutes water bodies. Large-scale accidents are also an important cause of pollution along shore lines. The most well-known example is the Exxon Valdez oil spill. Source: Oil spills. The basic organic chemicals industry is responsible for 82.53 t/yr of benzene, toluene, ethyl benzene, and xylene (BTEX) direct releases into water. Impact: Water quality deterioration, blocking sunlight from entering water bodies. • Organic compounds Description: Numerous compounds are included in this category. Research is focused on persistent organic compounds such as dioxins and polychlorinated biphenyls. PCBs are a group of theoretically 209 different compounds that are man-made, but now found all over the Earth due to their persistence and relative volatility. Due to their stability, they have been extensively used in many industrial applications (as hydraulic fluids or cooling liquids) and in open systems (as lubricants or paints). Another significant type of compounds included in this category is phenols. Phenols and similar compounds are primarily present in refinery and petrochemical wastewaters and are known to have a significant negative impact on marine life and human health, as they are well-known carcinogens (IRIS, 1998). Furthermore, phenols cause an increase in oxygen demand in water, and they also impart a taste to drinking water at very low concentrations of their chlorinated derivatives. Primary sources of phenols are in wastewaters from benzene-refining plants, oil refineries, coke plants, chemical operations, and plastics production. Another example is aniline (C6H7N), which is a colorless oily liquid and is highly toxic. It is used in the manufacture of antioxidants and vulcanization accelerators for the rubber industry, and the manufacture of dyes and pharmaceuticals (Sharp, 1990; IRIS, 1998). Another common contaminant type found in wastewater is dyes (Forgacsa et al., 2004). Synthetic dyes are extensively used in industry, e.g. textile manufacturing, leather tanning, paper production, and food technology.

26


Source: Agriculture, industrial processes. Specifically, industrial plants for the production of pulp from timber or other fibrous materials, and paper or board production are responsible of 70% of the total organic carbon released directly into water from industry per year. Impact: Depends on the exact type of the pollutants; from adverse long-term effects to immediate danger to human and biotic life. • Pathogenic Microorganisms Description: Pathogenic microorganisms include bacteria, viruses, and protozoans. Source: Untreated sewage, storm drains, run-off from farms, and particularly, boats that dump sewage. Impact: Many adverse effects on health. Typhoid, dysentery, and skin diseases are among the possible health effects. Commonly, wastewater contains numerous compounds and its exact composition is very difficult to determin or is even unknown, and therefore, its impact on the environment is characterized by • • • •

⫺ ⫺ 3⫺ the concentration of specific substances, such as NH⫹ 4 , NO 3 , NO 2 , and PO4 ions, and heavy metals sum parameters, such as TSS (total solids suspended), BOD (biological oxygen demand), COD (chemical oxygen demand), pH, conductivity and temperature. its toxicity to organisms in the receiver its hydraulic load.

In Table 1.12, the direct releases of the most important pollutants into water are presented in association with the corresponding main industrial source. For purposes of comparison, it is useful here to recall that road transport and combustion installations, mainly of the Table 1.12 The releases of the main pollutants released directly into water from the industrial sector in Europe (2001) Compound

t/yr

Main source

Phenols Total organic carbon

1,419,344 246,524

Nitrogen Phosphorus Chromium BTEX Nickel Copper Lead Polycyclic aromatic hydrocarbons Cadmium Arsenic Mercury

22,317 1662 864 82.5 71.5 45.8 41.7 10.3

Basic inorganic chemicals or fertilizers (47%) Industrial plants for pulp from timber or other fibrous materials, and paper or board production (70%) Basic inorganic chemicals or fertilizers (29%) Basic inorganic chemicals or fertilizers (25%) Metal industry (87%) Basic organic chemicals (56.1%) Metal industry (45%) Metal industry (23%) Metal industry (40%) Metal industry (74%)

8.1 5.1 0.5

Metal industry (66%) Metal industry (22%) Metal industry (23%)

1.3 Treatment Methods

27

energy sector, are the most important sources of air pollutants. In the case of water bodies, it is obvious that agriculture and the metal industry, plus the activities in the production of inorganic chemicals and fertilizers, constitute the major polluters. 1.3 TREATMENT METHODS The minimization of the releases into the environment can be largely achieved by • •

pollution prevention measures waste treatment (end-of-pipe techniques).

The first approach may involve cleaner synthesis processes, improved technology, recycling of residues, improved use of catalysts, and generally, every technique integrated into the process that leads to less waste; whereas the second one is an end-of-pipe treatment of the waste that is inevitably produced by a chemical process. Both approaches have to be combined so that our releases into the environment are as minimal and harmless as possible. Waste-treatment techniques are classified by the type of contaminant. The main techniques concerning waste gas treatment are the following. VOCs and inorganic compounds: membrane separation, condensation, adsorption, wet scrubbing, biofiltration, bioscrubbing, biotrickling, thermal oxidation, catalytic oxidation, and flaring. Particulate matter: separator, cyclone, electrostatic precipitator, wet dust scrubber, fabric filter, catalytic filtration, two-stage dust filter, absolute filter, high-efficiency air filter, and mist filter. Gaseous pollutants in combustion exhaust gases: dry sorbent injection, semidry sorbent injection, wet sorbent injection, selective noncatalytic reduction of NOx (SNCR), selective catalytic reduction of NOx (SCR). In Table 1.13, the conditions for the application of some treatment processes are shown. Table 1.13 Evaluation of alternative treatment processes used to control industrial vapor-phase pollutants Case

Activated carbons

Low VOC levels High VOC levels Continuous load Intermittent loads Halogenated organics T ⬎ 150 °F T⬍150 °F High flows Low flows High humidity Inorganic particles

Thermal oxidation

Scrubbers

Particulate filters

Catalytic oxidation

28


The main wastewater treatment techniques are Separation or clarification techniques: grit separation, sedimentation, air flotation, filtration, microfiltration and ultrafiltration, and oil–water separation. Physico-chemical treatment techniques: precipitation, sedimentation, air flotation, filtration, crystallization, chemical oxidation, wet air oxidation, super-critical water oxidation, chemical reduction, hydrolysis, nanofiltration, reserve osmosis, adsorption, ion exchange, extraction, distillation, rectification, evaporation, stripping, and incineration. Biological treatment techniques: anaerobic digestion processes, aerobic digestion processes, nitrification, denitrification, and central biological wastewater treatment. Adsorption, ion exchange, and catalysis share a great portion of environmental applications, as shown in the next section, and more extensively, in Chapter 2. Specifically, adsorption and catalysis are extensively used for the removal or destruction of air pollutants in gas streams as well as for purifying wastewaters or fresh water. Ion exchange has a special position among other techniques in the removal of heavy metals from wastewater.

1.4 ENVIRONMENTAL APPLICATIONS OF ADSORPTION, ION EXCHANGE, AND CATALYSIS Adsorption, ion exchange, and catalysis are discussed in this book. The first two are among the end-of-pipe techniques, whereas catalysis has a role to play in either preventing pollution during the process or as an end-of-pipe technique of waste treatment. Moreover, ion exchange is mainly used in wastewater treatment, whereas adsorption and catalysis can be found in both wastewater and gas management. Specifically, ion exchange is one of the best available techniques (BAT) suggested by EC for heavy metal and inorganic salts removal from wastewaters. As shown in Table 1.12, metal industry is the major source of metal releases into the environment and ion exchange can be employed as the main pollution abatement technology. We should again mention that nature does not have any efficient way of coping with these substances, and as a result, their discharge into the environment should be minimized. Adsorption is suggested as the BAT for both the minimization of contaminants in water that are unsuitable for biological treatment and the removal of VOCs and inorganic compounds from normal waste gas streams. Catalysis is also considered as the BAT for the destruction of water pollutants that are resistant to biological treatment (as catalytic wet-air oxidation) and for the oxidation of VOCs and inorganic compounds in gas streams (as catalytic oxidation). Generally, VOCs, sulfur oxides, nitrogen oxides, and various forms of hydrocarbons can be effectively treated by means of these processes.

MULTIPLE CHOICE QUESTIONS 1.

CFCs are involved in (a) photochemical smog (b) ozone depletion (c) greenhouse effect (d) both ozone depletion and greenhouse effect

1.4 Environmental Applications of Adsorption

29

2.

Nitrogen Oxides are involved in (a) photochemical smog (b) acid rain formation (c) greenhouse effect (d) all of the above

3.

N2O participates in (a) greenhouse effect and ozone depletion (b) greenhouse effect (c) ozone depletion (d) photochemical smog

4.

Comparing pollution from primary pollutants and secondary pollutants, (a) primary pollutants are generally easier to treat (b) secondary pollutants are generally easier to treat (c) it depends on the case (d) both kinds of pollution are equally difficult to treat

5.

During combustion NOx may come from (a) nitrogen in fuels (b) nitrogen in the atmosphere (c) both of the above (d) none of the above

6.

Generally, you would connect Athens and London, respectively, to which of the following types of air pollution: (a) photochemical smog and winter-type smog (b) winter-type smog and photochemical smog (c) winter-type smog and winter-type smog (d) photochemical smog and photochemical smog

7.

The main consumer of fresh water in Europe is (a) the public (b) agriculture (c) industry (d) energy sector

8.

Nitrates and phosphorus (a) are among inorganic pollutants (b) cause eutrophication (c) are persistent pollutants (d) are accumulated in the food chain

9.

Eutrophication is mainly attributed to (a) agriculture (b) organic industry (c) refineries (d) metal industry

30


10. Water pollution sources are divided into (a) diffuse and ducted sources (b) point and nonpoint sources (c) mobile and stationary sources (d) point and diffuse sources 11. The main source of air pollution is (a) road transport (b) metal industry (c) refineries (d) road transport and combustion installations 12. Among industries, the metal industry is the main source of (a) carbon monoxide (b) carbon dioxide (c) CFCs (d) NOx 13. The main sources of water pollutants are (a) combustion installations and metal industry (b) agriculture and combustion installations (c) agriculture, metal industry, and basic inorganic chemicals industry (d) metal industry and combustion installations 14. Acidification is a problem (a) that affects regions up to 100 km (b) that may appear on both regional and transboundary levels (c) faced all over the world (d) of specific areas ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

(d) (d) (a) (a) (c) (a) (d) (b) (a) (b) (d) (a) (c) (b)

both ozone depletion and greenhouse effect all of the above greenhouse effect and ozone depletion primary pollutants are generally easier to treat both of the above photochemical smog and winter-type smog energy sector cause eutrophication agriculture point and nonpoint sources road transport and combustion installations carbon monoxide agriculture, metal industry, and basic inorganic chemicals industry that may appear on both regional and transboundary levels

–2– Adsorption, Ion Exchange, and Catalysis

2.1 DEFINITIONS 2.1.1 Adsorption The term “sorption” is used to describe every type of capture of a substance from the external surface of solids, liquids, or mesomorphs as well as from the internal surface of porous solids or liquids (Skoulikides, 1989). Depending on the type of bonding involved, sorption can be classified as follows. (a) Physical sorption. In physical sorption (or physisorption), no exchange of electrons is observed; rather, intermolecular attractions between favorable energy sites take place and are therefore independent of the electronic properties of the molecules involved. Physisorption is characterized by interaction energies comparable to heats of vaporization (condensation). The adsorbate is held to the surface by relatively weak van der Waals forces and multiple layers may be formed with approximately the same heat of adsorption. The heat of adsorption for physisorption is at most a few kcal/mole and therefore this type of adsorption is stable only at temperatures below 150 °C. (b) Chemical sorption. Chemical sorption (or chemisorption) involves an exchange of electrons between specific surface sites and solute molecules, and as a result a chemical bond is formed. Chemisorption is characterized by interaction energies between the surface and adsorbate comparable to the strength of chemical bonds (tens of kcal/mol), and is consequently much stronger and more stable at high temperatures than physisorption. Generally, only a single molecular layer can be adsorbed. (c) Electrostatic sorption (ion exchange). This is a term reserved for Coulomb attractive forces between ions and charged functional groups and is commonly classified as ion exchange. The most important characteristics of physical and chemical sorption are presented in Table 2.1. The term “adsorption” includes the uptake of gaseous or liquid components of mixtures from the external and/or internal surface of porous solids. In chemical engineering, adsorption is called the separation process during which specific components of one phase of a fluid are transferred onto the surface of a solid adsorbent (McCabe et al., 1993).

31

32

2. Adsorption, Ion Exchange, and Catalysis

Table 2.1 Physical versus chemical sorption

Temperature range over which adsorption occurs Adsorption enthalpy

Nature of adsorption Saturation uptake Kinetics of adsorption

Chemisorption

Physisorption

Virtually unlimited; however, a given molecule may be effectively adsorbed only over a small range Wide range, related to the chemical bond strength— typically 40–800 kJ/mol

Near or below the condensation point of the gas (e.g. CO2 ⬍ 200 K)

Often dissociative and may be irreversible Limited to one monolayer Very variable; often is an activated process

Related to factors like molecular mass and polarity but typically 5–40 kJ/mol (i.e. ⬃ heat of liquefaction) Nondissociative and reversible Multilayer uptake is possible Fast, because it is a nonactivated process

When the species of the adsorbate travel between the atoms, ions, or the molecules of the adsorbent, the phenomenon of “absorption” takes place and this discriminates absorption from the main phenomenon of adsorption that takes place on the interface. The adsorption of various substances from solids is due to the increased free surface energy of the solids due to their extensive surface. According to the second law of thermodynamics, this energy has to be reduced. This is achieved by reducing the surface tension via the capture of extrinsic substances. Consider a molecule above a surface with the distance from the surface being normal to the surface. There are two competitive types of influence occuring: (a) repulsion between the cloud of electrons in the atoms that form the surface and those of the molecule and (b) van der Waals nuclear attraction force. The nuclear attraction has a much shorter radius of influence and as a result of the balance of these two forces, there is a “well” in the potential energy curve at a short distance from the surface, as shown in Figure 2.1. Molecules or atoms that reach this “well” are trapped or “adsorbed” by this potential energy “well” and cannot escape, unless they obtain enough kinetic energy to be desorbed. The surface can be characterized either as external when it involves bulges or cavities with width greater than the depth, or as internal when it involves pores and cavities that have depth greater than the width (Gregg and Sing, 1967). All surfaces are not really smooth and they exhibit valleys and peaks at a microscopic level. These areas are sensitive to force fields. In these areas, the atoms of the solid can attract atoms or molecules from a fluid nearby. The most important property of adsorbent materials, the property that is decisive for the adsorbent’s usage, is the pore structure. The total number of pores, their shape, and size determine the adsorption capacity and even the dynamic adsorption rate of the material. Generally, pores are divided into macro-, meso- and micropores. According to IUPAC, pores are classified as shown in Table 2.2. Porosity is a property of solids that is attributed to their structure and is evident by the presence of pores between internal supermolecular structures (Tager, 1978). It is not considered

2.1 Definitions

33

distance x

potential energy

molecule

distance x surface

Figure 2.1

The potential energy versus distance.

Table 2.2 The classification of pores according to their size (Rodriguez–Reinoso and Linares–Solano, 1989) Type

Pore diameter d (nm)

Macropores Mesopores Micropores Ultramicropores Supermicropores

d ⬎ 50 2 ⱕ d ⱕ 50 d⬍2 d ⬍ 0.7 0.7 ⬍ doa ⬍ 2

a

d0 is the pore width for slit-type pores or the pore diameter for cylindrical pores.

to be an intrinsic property of the solids, but depends on the treatment of the materials. The porosity can be developed by the aggregation of particles as well as by the detachment of a part of the mass of the solid. The pores shaped during the second process are comparable in shape and size with the particles detached. Adsorptive molecules transport through macropores to the mesopores and finally enter the micropores. The micropores usually constitute the largest portion of the internal surface and contribute the most to the total pore volume. The attractive forces are stronger and the pores are filled at low relative pressures in the microporosity, and therefore, most of the adsorption of gaseous adsorptives occurs within that region. Thus, the total pore volume and the pore size distribution determine the adsorption capacity. 2.1.2 Ion exchange Ion exchangers are solid materials that are able to take up charged ions from a solution and release an equivalent amount of other ions into the solution. The ability to exchange

34


ions is due to the properties of the structure of the materials. The exchanger consists of a so-called matrix, with positive or negative excess charge. This excess charge is localized in specific locations in the solid structure or in functional groups. The charge of the matrix is compensated by the so-called counterions, which can move within the free space of the matrix and can be replaced by other ions of equal charge sign (Helfferich, 1995). The pores sometimes contain not only counterions but also solvent. When the exchanger is in contact with the liquid phase, the solvent can travel through the exchanger and cause “swelling” to an extent that depends on the kind of counterions. Some electrolytes can also penetrate into the exchanger along with the solvent. As a result, there are additional counterions, the so-called coions, which have the same charge sign as the fixed ions. Normally, an exchanger has many open areas of variable size and shape that are altogether called “pores.” Only a few inorganic exchangers contain pores of uniform cross section. So, the exchangers exhibit a three-dimensional network of channels with irregular size. Although ion exchange is similar to sorption since a substance is captured by a solid in both processes, there is a characteristic difference between them: ion exchange is a stoichiometric process in contrast to sorption (Helfferich, 1995). It means that in the ion-exchange process, for every ion that is removed, another ion of the same sign is released into the solution. In contrast, in sorption, no replacement of the solute takes place. Ion exchange can be seen as a reversible reaction involving chemically equivalent quantities (Treybal, 1980; Perry and Green, 1999). The water-softening reaction Ca2⫹(aq) ⫹ 2Na⫹ (s) ; Ca2⫹(s)2 ⫹ 2Na⫹ (aq) constitutes a characteristic example of cation exchange. However, the characterization of an ion exchange as a “chemical process” is rather misleading. Ion exchange is in principle a redistribution of ions between two phases by diffusion, and chemical factors are less significant or even absent. The absence of any actual chemical reaction explains why the heat evolved in the course of an ion exchange is usually very small to negligible, often less than 2 kcal/mol (Helfferich, 1995). Only when an ion exchange is accompanied or followed by a reaction such as neutralization can the whole phenomenon be characterized as “chemical.” A characteristic example is in chelating resins where the ion exchange is followed by a chemical reaction and bond formation between the incoming ion and the solid matrix. Ion removal by solids could involve more phenomena, as for example in inorganic natural materials where ion uptake is attributed to ion exchange and adsorption processes or even to internal precipitation mechanisms (Inglezakis et al., 2004). 2.1.3 Catalysis Catalysis is one of the most important technologies in our world. It is used extensively in industries for production and in waste treatment for the removal of pollutants. Even our body constantly uses catalysis in biological processes. Enzymatic catalysis is necessary for all living matter. Most essential of all catalytic processes is photosynthesis, which is seen in most of the simplest and earliest evolved life forms.

2.1 Definitions

35

Catalysis can be generally described as the action of a catalyst, which is a substance that accelerates the rate of a chemical reaction, while itself remaining unchanged by the reaction. There are two main categories of catalysis: • •

homogeneous catalysis, where the catalyst is in the same phase with the reactants, heterogeneous catalysis, where the catalyst is present in a different phase from the reactants in the reaction.

Heterogeneous catalysis is often called surface catalysis as it mainly occurs between a solid surface and a gas. There are basically three stages in this process: • • •

adsorption (physisorption or chemisorption as described previously) of reactants on the catalyst surface, chemical reaction on the surface, desorption of products from the catalyst surface.

The following points have to be noted about the action of catalysts. First, catalysts do not alter the thermodynamics of the reactions. No catalyst favors a thermodynamically unfeasible reaction. Consequently, the reaction would proceed even without the catalyst, though perhaps too slowly to be observed or be of use in a given context. Furthermore, the use of a catalyst does not change the equilibrium composition because it increases the rates of the forward and reverse reactions by the same extent. Here arises the question: since a catalyst cannot change the position of equilibrium, why is it said that from the practical point of view, the most important characteristic of a catalyst is its selectivity? We should keep in mind that in the event of a complex reaction network, which is often the case, the catalyst might affect each reaction to a different extent, thus changing the overall reaction selectivity. For example, the reaction between gaseous ethanol and oxygen in nitrogen, at 150–300 °C under atmospheric pressure, is not just a simple oxidation reaction but a network consisting of the following reactions (Poulopoulos et al., 2002): C2 H 5 OH ⫹ 3O2 2CO2 ⫹ 3H 2 O

(2.1)

C2 H 5 OH ⫹ 0.5O2 CH 3 CHO ⫹ H 2 O

(2.2)

C2 H 5 OHCH 3 CHO ⫹ H 2

(2.3)

C2 H 5 OHC2 H 4 ⫹ H 2 O

(2.4)

2C2 H 5 OH(C2 H 5 )2 O ⫹ H 2 O

(2.5)

C2 H 5 OH2CH 4 ⫹ 0.5O2

(2.6)

Consequently, under these conditions, ethanol may be totally oxidized to carbon dioxide via the first reaction, partially oxidized to acetaldehyde via the second reaction, dehydrogenized

36


to acetaldehyde via the third reaction, dehydrated to ethylene via the fourth reaction, dehydrated to diethylether via the fifth reaction, and finally decomposed to methane via the last reaction. It is obvious that what could be misunderstood as a simple oxidation is really a complex scheme of reactions. The presence of a catalyst may enhance one or more of these reactions or even all of them by various degrees, leading to a different overall selectivity. Its selection would be made on the basis of the desired products, and catalyst selectivity is the key characteristic to practical applications. So, catalysts enable reactions to occur much faster and allow the use of milder conditions of temperature for achieving reaction rates of practical use. They achieve this by providing an alternative pathway of lower activation energy for the reaction to proceed. As shown in Figure 2.2, a catalyst lowers the energy of the transition state without changing the energy of the reactants and products. For example, the uncatalyzed value of the activation energy of the decomposition of nitrogen oxide to nitrogen and oxygen is 1240 kJ/mol, whereas with a gold catalyst this becomes ⫹120 kJ/mol. Since the catalyzed path requires lower activation energy, more molecules will have sufficient energy to react effectively than in the case of the uncatalyzed path. In homogeneous catalysis, this is generally achieved by the reaction between the catalyst and one or more reactants to form an unstable chemical intermediate, which subsequently reacts to produce the final product. The catalyst is regenerated in the final step. For example, if reactant A reacts with B to form the product (P) in the presence of a catalyst (C), a possible reaction scheme is A ⫹ CAC B⫹ ACP ⫹ C A ⫹ B C →P An example of great environmental interest is the catalytic mechanism for ozone destruction by the hydroxyl radical, which is believed to be ⭈OH ⫹ O3HO2 ⭈⫹O2 HO2 ⭈⫹O3⭈OH ⫹ 2O2

potential energy

The hydroxyl radical is regenerated in the second reaction and may continue its action.

uncatalysed reaction path

catalysed reaction path

Reaction coordinate

Figure 2.2

The catalyzed and the uncatalyzed reaction path.

2.2 Historical Aspects

37

In heterogeneous catalysis, the catalyst provides a surface on which the reactants are adsorbed. The chemical bonds of the reactants become weakened on the catalytic surface and new compounds are formed. These compounds (products) have weaker bonds with the catalyst and consequently are released. An example of heterogeneous catalysis is the industrial synthesis of ammonia, which requires solid catalysts to obtain significant rates of reaction between nitrogen and hydrogen: N 2 (g)N 2 (ads) (adsorption of nitrogen on catalyst surface) N 2 (ads)2N(ads) (dissociation of nitrogen) H 2 (g)H 2 (ads) (adsorption of hydrogen on catalyst surface) H 2 (ads)2H(ads) (dissociation of hydrogen) N(ads) ⫹ 3H(ads)NH 3 (ads) (reaction to form ammonia adsorbed) NH3 (ads)NH3 (g) (desorption of ammonia) It has to be noted that the adsorption of reactants is generally not uniform across the catalyst surface. Adsorption, and therefore catalysis, takes place mainly at certain favorable locations on a surface called active sites. In environmental chemistry, catalysts are essential for breaking down pollutants such as automobile and industrial exhausts.

2.2 HISTORICAL ASPECTS 2.2.1 Adsorption The first known use of adsorption was made in 3750 B.C. by Egyptians and Sumerians who used charcoal for the reduction of copper, zinc, and tin ores for the manufacture of bronze. Around 1550 B.C., Egyptians applied charcoal for medicinal purposes, whereas around 460 B.C., Hippocrates and Pliny introduced the use of charcoal to treat a wide range of infections. Around the same age, Phoenicians used charcoal filters to treat drinking water. So, this must have been the first use of adsorption for environmental purposes. In 157 B.C., Claudius Galen introduced the use of carbons of vegetable and animal origin to treat a wide range of complaints. These early applications of adsorption were based on intuition and not on a systematic study. It was in 1773 that Scheele made the first quantitative observations in connection with adsorption, whereas F. Fontana in 1777 reported his experiments on the uptake of gases from charcoal and clays. However, the modern application of adsorption is attributed to Lowitz. Lowitz used charcoal for the decolorization of tartaric acid solutions in 1788. The next systematic studies were published by Saussure in 1814. He concluded that all types of gases can be taken up by a number of porous substances and this process is accompanied by the evolution of heat (Dabrowski, 2001).

38


The term “adsorption” was first used by H. Kayser in 1881. J. W. McBain introduced a similar term in 1909, i.e. “absorption”, to determine an uptake of hydrogen by carbon much slower than adsorption. He proposed the term “sorption” for adsorption and absorption (Dabrowski, 2001). In 1903, Tswett was the first to study selective adsorption. He investigated the separation of chlorophyll and other plant pigments using silica materials. This technique proposed by Tswett has been called “column solid–liquid adsorption chromatography.” However, there was no sound theory that enabled the interpretation of adsorption isotherm data untill 1914. Despite the fact that the Freundlich equation was used, there was no theoretical justification for it. It was an empirical equation, proposed actually by van Bemmelen in 1888. However, it is today known as the Freundlich equation because Freundlich assigned great importance to it and popularized its use. Langmuir was the first to have introduced a clear concept of the monomolecular adsorption on energetically homogeneous surfaces in 1918 and derived the homonymous equation based on kinetic studies (Dabrowski, 2001). The first practical applications of adsorption were based on the selective removal of individual components from their mixtures using other substances. The first filters for water treatment were installed in Europe and the United States in 1929 and 1930, respectively. Activated carbon was recognized as an efficient purification and separation material for the synthetic chemical industry in the 1940s. By the late 1960s and early 1970s, activated carbon was used in many applications for removing a broad spectrum of synthetic chemicals from water and gases. In Table 2.3, the history of adsorption is presented briefly. 2.2.2 Ion exchange The first citation of an application of ion exchange can be found in Aristotle’s Problematica, where it is mentioned that sand filters were used for the purification of sea and impure drinking waters. That is also the first environmental application. In the same book, Aristotle suggested that desalination resulted from density effects. It seems that practical applications of ion exchange were well recognized before the 19th century. However, the underlying physical phenomenon was not known. Credit for the identification of the ion-exchange phenomenon is attributed to two agriculture chemists, Thomson and Way. In 1848, Thomson reported to Way that he had found that urine was decolorized and deodorized during the filtration of liquid manure through a bed of an ordinary loamy soil. It was Way, who illustrated the basic characteristics of ion exchange after conducting several experiments (Lucy, 2003). After soil and clays, natural and synthetic aluminum silicates and synthetic zeolites were tested as ion-exchange materials by other scientists. However, the first practical applications of ion exchange took place in the early 20th century. The first synthetic organic resins were synthesized in 1935. This spectacular evolution began with the finding of two English chemists, Adams and Holmes, who found that crushed phonograph records exhibited ion-exchange properties (Helfferich, 1962). Much progress was made during World War II in the field of ion exchange, but the results


39

Table 2.3 Brief history of adsorption (Dabrowski, 2001) Scientist(s) Name(s)

Breakthrough

C. W. Scheele, F. Fontana T. Lowitz D. M. Kehl

Experiments on the uptake of gases by charcoal and 1773–1777 clays Decolorization of tartaric acid utilizing charcoal 1776–1778 Application of carbons of animal origin for the removal 1793 of colors from sugar. The English sugar industry used charcoal as a decolorization agent in 1794 Systematic studies on adsorption. He discovered the 1814 exothermic character of adsorption Introduced the term “adsorption” 1881 The Freundlich equation was first proposed by van 1888 Bemmelen and popularized by Freundlich Set the basis for the commercial development 1901 of activated carbons Discovered selective adsorption. He used the term and 1903 technology of “column solid–liquid adsorption chromatography” Found selective adsorption of oxygen from a mixture 1904 with nitrogen, during the uptake of air by charcoal Applied the use of active carbons as an adsorption 1915 medium in a gas mask for the needs of World War I Derived the concept of monolayer adsorption, formed 1918 on energetically homogeneous solid surfaces.Was awarded the Nobel Prize in chemistry in 1932 The milestone in the development of adsorption 1938 science was the multilayer isotherm equation, known as BET Introduced to laboratory practice the solid–liquid 1941 partition chromatography, both in column and planar form Invented the method of zeolite synthesis. In the 1956 same year, the North-American Linde Company started the production of synthetic zeolites on a commercial scale

T. de Saussure H. Kayser Van Bemmelen H. Freundlich R. Von Ostreyko M. S. Tswett J. Dewar W. A. Zelinsky I. Langmuir S. Brunauer P. H. Emmet, E. Teller A. J. P. Martin B. L. M. Synge R. M. Barrer D. W. Breck

Year

obtained were not published for some years due to reasons of confidentiality. Afterwards, there was a rapid development of ion-exchange materials and methods (Lucy, 2003). A brief history of ion exchange is presented in Table 2.4. 2.2.3 Catalysis Catalysis is not a new phenomenon, but its intentional utilization by humans has begun only in this century. One of the first catalytic processes was probably the fermentation of fruits to obtain alcoholic beverages. Enzymes found in yeast were used as catalysts for the conversion of sugar into alcohol. In fact, ancient Sumerians described beer preparation on

40


Table 2.4 Brief history of ion exchange (Lucy, 2003; Helfferich, 1962) Scientist(s) name(s)

Breakthrough

H. S. Thompson

Thompson passed a solution of manure through a filter made of ordinary garden soil and found that the ammonia was removed from solution. Recognition of the phenomenon of ion exchange and a description of its basic characteristics. The ion exchange property of soils was found to be based on their containing small amounts of zeolites. Proved that the adsorption of ions by clays and zeolites constitutes a reversible reaction. Zeolites recognized as carriers of base exchange in soils; equivalence of exchange of bases proved. Artificial zeolites used for removal of potassium from sugar juices. First synthetic industrial ion exchanger. Manufacture of sulfonated coals and suggestion for the removal of potassium from sugar juices. Discovered that zeolites could be used to soften hard water. He also invented processes for synthesizing zeolites and designed the equipment—the zeolite water softener— used for the recovery of gold from sea water. The first analytical application of ion exchange. The first use of ion exchange in column chromatography. The first use of an ion-exchange column for anion analysis. Entirely new types of cation exchangers were developed. Not only could they be used in the sodium cycle when regenerated with salt, but also in the hydrogen cycle when regenerated with an acid. One group of these cation exchangers was the carbonaceous type, which was made by the sulfonation of coal. Synthesis of the first organic ion exchanger.

H. S. Thompson J. T. Way J. Spence H. Eichorn J. Lemberg F. Harm A. Rumpler S. Mayert K. Halse R. Gans

O. Folin, R. Bell J. Whitehorn A. Bahrdt O. Liebknecht P. Smit

B. A. Adams E. L. Holmes G. F. D’Alelio G. E. Boyd J. Schubert A.W. Adamson C. H. McBurney A. Skogseid J. A. Marinsky L. E. Glendenin C. D. Coryell D. K. Hale D. Reichenberg N. E. Topp C. G. Thomas R. M. Barrer D. W. Breck

Invention of sulfonated polystyrene polymerization cation exchangers. Demonstration of the applicability of ion exchange for adsorption of fission products in trace amounts (lanthanides). Invention of aminated polystyrene polymerization anion exchangers. Preparation of a potassium-specific polystyrene cation-exchanger chelating resin. The discovery of promethium (element 61), an element not found in nature, is attributed to ion exchange Development of carboxylic addition polymers as weak acid cation exchangers. New zeolites as molecular sieves with ion-exchange properties.

Year 1845 1848–1852

1858 1876 1901–1902

1905

1917 1923 1927 1934–1939

1934–1935 1942 1942 1947 1947 1947 1949–1956

1951–1956

(Continued)


41

Table 2.4 (Continued) Scientist(s) name(s)

Breakthrough

H. P. Gregor Invention and development of chelating polymers. K. W. Pepper L. R. Morris M. A. Peterson, H. A. Sober Development of cellulose ion exchangers. 1956-58 Preparation and studies of nonsiliceous inorganic ion exchangers—insoluble salts, heteropolyacids F. Helfferich Foundations laid for the new theoretical treatment of ion exchange. T. R. E. Kressmann Invention and development of isoporous ion-exchange J. R. Millar resins. J. Weiss Thermally regenerable ion-exchange resins and water desalination based on them.

Year 1952–1971 1956 1956 1959 1960 1964

clay plates many millennia ago. A few examples of the utilization of catalysis in ancient civilizations are the following: • • • •

6000 B.C.—beer brewing by malting procedure (malt enzymes) 3000 B.C.—wine making by fermentative conversion of grape juice sugars 2000 B.C.—making alcohol by fermentation of various carbohydrate sources 800 B.C.—cheese making by casein hydrolysis with calf stomach extract (calf rennet)

The phenomenon under consideration was studied systematically in the beginning of the 19th century. In 1815, Davy performed experiments that dealt with catalytic combustion on platinum gauzes. The term “catalysis”, however, was introduced by Berzelius in 1836. He first defined a catalyst (Berzelius, 1836) as “a compound, which increases the rate of a chemical reaction, but which is not consumed during the reaction.” This definition was later amended by Ostwald (1853–1932) in 1895 to involve the possibility that small amounts of the catalyst are lost in the reaction or that the catalytic activity is slowly decreased: “A catalyst is a substance that increases the rate of approach to equilibrium of a chemical reaction without being substantially consumed in the reaction.” It was more than a century after Berzelius’ first definition that Marcel Prettre’s introduced the notion of yield: “The catalyst is a substance that increases the rate of a chemical transformation without modifying the yield, and that is found intact among the final products of the reaction.” It is fascinating that even today, heterogeneous catalysis still remains an empirical science. Although the application of catalysts in the chemical industry is a fact for at least 150 years, the experimental techniques for investigation of catalysis at the atomic level did not become routine until less than 25 years ago; the computational techniques are even younger and have hardly become routine yet. For this reason, a vast amount of empirical knowledge exists and awaits systematic investigation. A short history of heterogeneous catalysis is presented in Table 2.5.

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Table 2.5 Brief history of heterogeneous catalysis Scientist(s) name(s)

Breakthrough

Year

von Marum J. Dalton

Studies the dehydrogenation of alcohols using metals. Suggests that chemical compounds consist of molecules and molecules consist of atoms. Studies the oxidation of methane on platinum wires. Studies oxidations catalyzed by platinum adsorbed on clay pellets. Studies the ignition of hydrogen in air at platinum surfaces. Formulates the definition of catalysis. Formulates the concept of valency. Formulation of the law of mass action.

1796 1808

H. Davy W. Henry M. Faraday J. Berzelius E. Frankland C. W. Guldberg P. Wage von Hoffmann

Develops Ag as a catalyst for the oxidation of CH3OH to HCHO. R. Messel Develops the industrial oxidation of SO2 catalyzed by Pt J. W. Gibbs Publishes “On the equilibrium of heterogeneous substances”,which contains Gibbs’ phase law C. Winkler Invention of the contact process for the synthesis of sulfuric acid. Badische Anilin and Industrial synthesis of sulfuric acid using a platinum Soda Fabrik Germany catalyst W. Ostwald Discovers that the reaction 2NH3 ⫹ 5/2O2 = 2NO ⫹ 3H2O is catalyzed by Pt. S. Sabatier Studies hydrogenation of alkenes catalyzed by Ni (1902–1905). F. Haber Reports the production of small amounts of NH3. from N2 ⫹ 3H2 using an iron catalyst W. Ostwald Receives the Nobel prize in chemistry for his work on catalysis, chemical equilibrium, and the rate of chemical reactions. P. Sabatier Receives the Nobel prize in chemistry for the development of the hydrogenation of organic compounds catalyzed by small metal particles. I. Langmuir Formulates a theory of adsorption. Chemical Builds an industrial nitric acid plant based on the Construction Co. Ostwald process. J. Frenkel Publishes a theory of adsorption. H. S. Taylor Theory of catalysis. I. Langmuir Formulate the principles of Langmuir– W. Hinschelwood Hinschelwood kinetics. I. Langmuir Receives the Nobel prize in chemistry for his work on surface chemistry G. Damköhler Introduces the Damköhler group. E. W. Thiele Introduces the effectiveness factor and the Thiele modulus. H. Kramers Publishes the definitive treatment of kinetics.

1817 1824 1825 1836 1852 1867 1869 1875 1876 1879 1889 1901 1902 1905 1909 1912 1915 1917 1924 1925 1927 1932 1937 1939 1940

(Continued)

2.3 Adsorption, Ion Exchange, and Catalysis: Three Related Phenomena

43

Table 2.5 (Continued) Scientist(s) name(s)

Breakthrough

Year

G. Natta

Continues the study initiated by Karl Ziegler on metal-organic catalysts for polymerization of alkenes Publishes a theory of precursor kinetics for chemisorption. Catalytic converters are introduced in new cars in the United States. Measure the rate of a reaction catalyzed by a single crystal (methanation, Ni single crystals)

1953

P. Kisliuk

R. Kelley, D. Goodman

1957 1975 1982

2.3 ADSORPTION, ION EXCHANGE, AND CATALYSIS: THREE RELATED PHENOMENA At a first glance, adsorption, ion exchange, and catalysis are three different phenomena with diverse characteristics. However, despite these differences, there are many common features among these processes. In the following sections, a relationship between them will be attempted. 2.3.1 Adsorption and ion exchange Ion exchange is similar to adsorption, since mass transfer from a fluid to a solid phase is common in both processes, i.e. they are basically diffusion processes. Ion exchange is also a sorption process, but ions are the sorbed species in contrast to adsorption, where electrically neutral species are sorbed (Noble and Terry, 2004; Perry and Green, 1999). It is generally accepted that adsorption and ion exchange can be grouped together as sorption for a unified treatment in practical applications. Most of the mathematical theories and approaches have been developed originally for sorption rather than ion exchange. However, they are sufficiently general to be applicable with minor, if any, modifications to a number of similar phenomena such as ion exclusion and ligand exchange. According to Helfferich (1995), the applicability of a simplified theory depends more on the mode of operation than on the particular mechanism of solute uptake. A significant feature of physical adsorption is that the rate of the phenomenon is generally too high and consequently, the overall rate is controlled by mass (or heat transfer) resistance, rather than by the intrinsic sorption kinetics (Ruthven, 1984). Thus, sorption is viewed and termed in this book as a “diffusion-controlled” process. The same holds for ion exchange. 2.3.2 Catalysis and adsorption As discussed earlier, the first step in heterogeneous catalysis is the adsorption of the molecules of the reactants on the surface of the adsorbent or of the catalyst (inner and outer surfaces). Then, molecular dissociation of at least one or two reacting components takes place, usually preceded by surface diffusion. The next step is a surface reaction, which is

44


often the rate-determining step in a catalytic reaction. Then, desorption of the product occurs because the surface bond is broken, and the final product enters the bulk phase, diffusing through and out of the catalyst pores. This very simple picture highlights the basic idea of heterogeneous catalysis. Consequently, the development of catalysis is closely related to the evolution of adsorption. Most of the adsorbents used in the adsorption process are also useful to catalysis, because they can act as solid catalysts or their supports. The basic function of catalyst supports, usually porous adsorbents, is to keep the catalytically active phase in a highly dispersed state. It is obvious that the methods of preparation and characterization of adsorbents and catalysts are very similar or identical. The physical structure of catalysts is investigated by means of both adsorption methods and various instrumental techniques derived for estimating their porosity and surface area. Factors such as surface area, distribution of pore volumes, pore sizes, stability, and mechanical properties of materials used are also very important in both processes—adsorption and catalysis. Activated carbons, silica, and alumina species as well as natural amorphous aluminosilicates and zeolites are widely used as either catalyst supports or heterogeneous catalysts. From the above, the following conclusions can be easily drawn (Dabrowski, 2001): • • • • •

adsorption and catalysis are closely related to each other, the action of solid catalysts results from their capacity to adsorb reacting substances, the same porous solids can be used as adsorbents, catalyst supports, and catalysts, the chemical character and size of solid surface areas, their porous structure, mechanical properties, and thermal stability play an essential role in both adsorption and catalysis, the development of theoretical studies on adsorption, design, and manufacture of new adsorbents affects heterogeneous catalysis development.

2.3.3 Catalysis and ion exchange Catalysis of reactions by ion exchangers can be explained in terms of the catalytic activity of the exchanging ions and is analogous to homogeneous-phase catalysis by dissolved electrolytes (Hellferich, 1995). Ion-exchange resins can act as insoluble acids and bases for the catalysis of chemical reactions. Heterogeneous catalysis with resins can be carried out in aqueous or nonaqueous solvent solutions (Guzzo, 1997). It is interesting that ion exchangers can be used for catalyzing reactions in the gas phase. However, its relation to the ion-exchange properties of the catalyst is less distinct. Despite the presence of two phases, solid and liquid, catalysis by ion exchangers is not a true case of heterogeneous catalysis and may be described more adequately as homogeneous catalysis in the pore phase. This is because the ions of the exchangers that are involved in the catalytic reaction are dissolved in the pores of the solid, where they act as in a homogeneous solution (Helfferich and Hwang, 1988). A good example is the acidcatalyzed hydrolysis of cane sugar, which is carried out in solution using a strong acid cation-exchange resin in its hydrogen form. The “inversion” of sucrose is a commercial process in which ion-exchange catalysis has been widely used (Purolite Co.). Despite the fact that it is not clear whether the catalytic activity is related to the ability of the solid to act under different conditions as an ion exchanger, there is a variety of

2.3 Adsorption, Ion Exchange, and Catalysis: Three Related Phenomena

45

materials used in both catalysis and ion exchange, such as zeolites and clays. Some catalytic applications of zeolites and clays, materials used as ion exchangers, are presented in Table 2.6. Furthermore, in the manufacture of zeolite catalysts, ion exchange plays an outstanding role. Brönsted acid sites can be readily generated by introducing ammonium ions followed by a heat treatment or by introducing multivalent metal cations, again followed by heat treatment (Weitkamp, 2000). However, not all these applications incorporate the ion exchange and catalysis phenomena at the same time, i.e. simultaneous action of these two mechanisms. Catalysis by ion exchangers exhibits some advantages over homogeneous catalysis by dissolved electrolytes, such as • • • •

the catalyst can be easily separated from the liquid phase by filtration or other suitable means, continuous operation in fixed beds is possible, in some cases, it is possible to isolate reaction intermediates that cannot be obtained by homogeneous catalysis, the ion exchanger is generally more selective, i.e. it distinguishes more sharply between the various reactant molecules than the dissolved catalyst.

Table 2.6 Catalytic reactions for which zeolites and clays are used (Tsitsishvili et al., 1992; Mumpton, 1999; Armbruster, 2001; Chitnis and Sharma, 1997) Material

Application

Natural erionite-clinoptilolite Hydrogen-exchanged natural mordenite

Selective-forming catalyst Hydrocarbon conversion catalyst for the deprotonization of toluene to benzene and xylene Hydromethylation of toluene Isomerization of n-butene, the dehydration of methanol to dimethyl ether, and the hydration of acetylene to acetaldehyde Alcylation reactions (e.g. of benzene with benzyl chloride) Dimerization reactions (e.g. of a-methylstyrene) Etherification reactions (e.g. of tert-butanol with methanol) Condensation reactions (e.g. of cyclohexanone) Separation of close boiling aromatic amines Separation of isomers of xylene Dimerization of unsaturated fatty acids to dimer acids Removal of olefins from “BTX”, ethylbenzene, cumene, etc. steams Decolorization of industrial oil derivatives Purification of kerosene and other mineral oil derivatives

Cation-exchanged clinoptilolite Clinoptilolite Acid-treated clays

Thermally pillared clays

46


Catalysis with ion-exchange resins provides effective and efficient answers to a number of catalytic problems: • • • •

the resins do not introduce counterions (which would have to be removed from the final product made under homogeneous catalysis conditions), they may be regenerated and reused over relatively long periods, corrosion arising from the presence of strong acids in the bulk phase is eliminated, resins may be tailored in particle size, pore volume, surface area, swelling in solvents, etc.

However, it should be noted that the maximum operating temperature recommended for ion-exchange resin catalysts is in the range 137–145°C. Thus, the use of resins as catalysts is limited to systems that operate at relatively low temperatures. In the case of elevated temperatures, zeolites could be used instead, because they exhibit higher stability for temperatures as high as 800°C (e.g. clinoptilolite). In many applications, acid-treated clays could be used as an alternative. Clays, being naturally occurring aluminosilicates, are readily available and inexpensive compared to other types of heterogeneous acid catalysts, e.g. ion-exchange resins (Chitnis and Sharma, 1997). Clays, in general, are thermally stable up to 200 °C and they can be greatly improved by the pillaring process. Furthermore, in many cases, they exhibit higher selectivity than resins. The disadvantage of clays is that their activity is lower than resins, their use is restricted to nonaqueous reactions systems only, and their mechanical strength is low.

2.4 ENVIRONMENTAL APPLICATIONS OF ADSORPTION, ION EXCHANGE, AND CATALYSIS 2.4.1 Adsorption There are many environmental applications of adsorption in practice and many others are being developed (Noble and Terry, 2004). Activated carbons and clays are frequently used for the removal of organic contaminants, such as phenol and aniline, both of which are prevalent in industry wastewaters and are known to have a significant negative impact on marine life and human health (IRIS, 1998; Dabrowski et al., 2005). Moreover, the adsorption on inexpensive and efficient solid supports has been considered a simple and economical viable method for the removal of dyes from water and wastewater (Forgacsa et al., 2004). Activated carbon, clays, coal, vermiculite, and other adsorbents have been used for this purpose. Specifically, adsorption can be employed in (Noble and Terry, 2004; Dabrowski, 2001): • • • • •

the removal of water from organic solvents the removal of organics from water taste and odor regulation in wastewater treatment the removal of radon, hydrogen sulfide, and other sulfur compounds from gas streams mercury removal from chlor-alkali-cell gas effluent

2.4 Environmental Applications of Adsorption, Ion Exchange, and Catalysis

• • • • •

47

heavy-metal removal in clay barriers nitrogen and phosphorus removal from wastewater, i.e. removal and recovery of nutrients solvent recovery and solvent vapor fractionation volatile organic compounds recovery from gas streams and groundwater water removal from gas streams containing acid gases

Other important applications of adsorption are the control of “greenhouse” gases (CO, CH4, N2O), the utilization of CH4, the flue gas treatment (SOx, NOx, Hg removal), and the recovery of the ozone-depleting CFCs (Dabrowski, 2001). Activated carbons and hydrophobic zeolites are used for the adsorption of HCFCs (Tsai, 2002). The most commonly used adsorbents are shown in Table 2.7. The adsorption process can be used for substance recovery as well as for the abatement of undesirable emissions in wastewaters (Table 2.8) and gas streams (Table 2.9). Table 2.7 The most common adsorbents Wastewater treatment

VOC removal

Activated carbon (mainly as granulates) Lignite coke Activated aluminum oxide Adsorber resins Zeolites

Granular activated carbon Zeolites Macroporous polymer particles Silica gel Sodium–aluminum silicates

Table 2.8 Representative commercial liquid-phase adsorption separations Liquid bulk separations (adsorbate concentration in the feed ⬎10% wt.)

Adsorbent

Fructose/glucose p-Xylene/o-xylene, m-xylene Detergent-range olefins/paraffins Normal paraffins/isoparaffins, aromatics p-Diethyl benzene/isomer mixture

Zeolites Zeolites Zeolites Zeolites Zeolites

Liquid Purifications (adsorbate concentration in the feed ⬍3% wt.)

Adsorbent

Sulfur compounds/organics Organics/H2O Odor, taste/drinking H2O H2O/organics Decolorizing petroleum fractions, sugar syrups, vegetable oils, etc.

Zeolites Activated carbon Activated carbon Silica, alumina, zeolite Activated carbon

48


Table 2.9 Representative commercial gas-phase adsorption separations Gas bulk separations (adsorbate concentration in the feed ⬎10% wt.)

Adsorbent

N2/O2 O2/N2 H2O/ethanol CO, CH4, CO2, N2, NH3/H2 Acetone/vent streams C2H4/vent streams

Zeolite Carbon molecular sieve Zeolite Zeolite, activated carbon Activated carbon Activated carbon

Gas purifications (adsorbate concentration in the feed ⬍3% wt.)

Adsorbent

H2O/olefin-containing cracked gas, natural gas, air, synthesis gas SO2/vent streams CO2/C2H4, natural gas Organics/vent streams Sulfur compounds/natural gas, hydrogen, liquefied petroleum gas (LPG) Solvents/air Odors/air NOx/N2

Silica, alumina, zeolite Zeolite Zeolite Activated carbon and others Zeolite Activated carbon Activated carbon Zeolite

As any process, adsorption has both some advantages and disadvantages: Advantages • • • • • •

high removal efficiency enables removal of refractory and/or toxic organic compounds possibility of compounds recovery (preferably with zeolites) simple installation and maintenance capability of systems for fully automatic operation a large variety of adsorbents available

Disadvantages • • • • • •

adsorbents deteriorate in capacity gradually particulates in the feed can cause problems high content of macromolecular compounds decreases efficiency and may cause irreversible blockage of active sites risk of bed fires in the VOC abatement spent adsorbent has to be regenerated (high energy consumption) or disposed (causing waste) relatively high capital cost


49

Special applications: The environmental control and life support system on a spacecraft maintains a safe and comfortable environment, in which the crew can live and work, by supplying oxygen and water and by removing carbon dioxide, water vapor, and trace contaminants from cabin air. It is apparent that the processes aimed at the recycling of air and water are vital for supporting life in the cabin. These recycling processes include separation and reduction of carbon dioxide, removal of trace gas-phase contaminants, recovery and purification of humidity condensate, purification and polishing of wastewater streams, and are performed totally or in part by adsorption equipment (Dabrowski, 2001). Another special application of adsorption in space is presented by Grover et al. (1998). The University of Washington has designed an in situ resource utilization system to provide water to the life-support system in the laboratory module of the NASA Mars Reference Mission, a piloted mission to Mars. In this system, the Water Vapor Adsorption Reactor (WAVAR) extracts water vapor from the Martian atmosphere by adsorption in a bed of type 3A zeolite molecular sieve. Using ambient winds and fan power to move atmosphere, the WAVAR adsorbs the water vapor until the zeolite 3A bed is nearly saturated, and then heats the bed within a sealed chamber by microwave radiation to drive off water for collection. The water vapor flows to a condenser where it freezes and is later liquefied for use in the life-support system.

2.4.2 Ion exchange Although there are some applications in gas emissions reduction, for example, hydrogen sulfide and ammonia removal by utilizing carboxylic acid resins and ammonium anionexchange resins, ion exchange is mainly used in wastewater treatment. Some characteristic environmental applications are the following (Noble and Terry, 2004): • • •

•

treatment of mine drainage water: removal of metal cations and anions using silicotitanates and layered titanates removal of nitrates and ammonia from groundwater treatment of nuclear waste solutions: (1) strontium removal by clinoptilolite and heulandite (Chernjatskaja, 1988), (2) cesium removal using hexacyanoferrate exchanger and phenolic resins (Harjula et al., 1994; Samanta et al., 1992), (3) treatment of liquid nuclear wastes using titanate ion exchangers (Dosch et al., 1993), and (4) thorium ions removal using zeolites (Sinha et al., 1994) plating industry: (1) treatment of raw water to produce high-quality rinse water, (2) chemical recovery from rinse water, (3) treatment of plating baths to remove contaminants, and (4) as a primary end-of-pipe treatment process

50


Some of the cons and pros of ion exchange are Advantages • • • •

in principle, all ions or ionizable species can be removed from aqueous liquids recovery of valuable species is possible high efficiency a large variety of specific resins is available

Disadvantages • • •

prefiltration is required (suspended particles in the feed should be less than about 50 mg/L to prevent plugging) interference of competing cations in the wastewater low-temperature resistance of organic (resin) ion exchangers

Special application: Ion exchange was in the foreground in World War II during the Manhattan Project. The need for separation of reactor fusion products for analysis purposes led Boyd and coworkers to suggest the use of resins for the uptake of several fusion products. This study paved the way for the development of several ion-exchange methods. However, the results of the Manhattan Project in connection with ion exchange were not published until 1947 on grounds of confidentiality. 2.4.3 Catalysis Catalysis has been widely used in numerous industrial processes for at least 150 years. Catalysts are selected to increase the reaction rate and the yield of the desired products. Heterogeneous catalysis is largely an empirical science, more of an art than a science, and a large amount of knowledge in this field is empirical. The great range of catalyst applications led to the development of various practical rules concerning their selection and use, much earlier than the advance in the experimental techniques for the investigation of catalysis. The emphasis on environmental protection in the last three decades, as industrial and economic growth gave birth to many forms of pollution threatening human health and Earth ecosystems, resulted in the growth of environmental catalysis. So, catalysts are not only used to promote processes in the production field, but also to reduce the emissions of undesirable or hazardous compounds to the environment. For example, catalytic combustion has been proposed and developed as an effective method for controlling the emissions of hydrocarbons and carbon monoxide. In fact, most of us benefit from the use of catalysis. Automotive catalytic converters have represented the most massive application of environmental catalysis and one of the most challenging and successful cases in catalysis, generally. Automobile catalysts deserve a few more comments. The engine exhaust emission is a complex mixture, whose composition and flow rate change continuously depending on a variety of factors such as driving conditions, acceleration, and speed. Despite the variability of the conditions, three-way catalysts have achieved the reduction of exhaust carbon monoxide, hydrocarbons, and


51

nitrogen oxides by over 70%. Today, about one-third of the world market for catalysts involves environmental catalysis. Generally, catalysts are called into action to eliminate emissions from mobile (cars) and stationary (industry) sources, to take part in liquid and solid waste treatment, and contribute to the effort to reduce volatile organic compounds and gases that pose major environmental problems such as photochemical smog and (at a global level) the greenhouse effect. The use of catalysts for exploiting renewable energy sources, producing clean fuels in refineries, and minimizing the by-product formation in industry also fall within the definition of environmental catalysis. In the future, the continuous effort to control transport emissions, improve indoor air quality, and decontaminate polluted water and soil will further boost catalytic technology. All in all, catalysts will continue to be a valuable asset in the effort to protect human health, the natural environment, and the existence of life on Earth. There are, however, some distinctive differences between the environmental and the other aspects of catalysis. First, the feed and operation conditions of environmental catalysts cannot be changed in order to increase conversion or selectivity, as commonly done for chemical production catalysts. Second, environmental catalysis has a role to play not only in industrial processes, but also in emission control (auto, ship, and flight emissions), and even in our daily life (water purifiers). Consequently, the concept of environmental catalysis is vital for a sustainable future. Last but not least, environmental catalysts often operate in more extreme conditions than catalysts in chemical production. There are also cases, such as automotive vehicles, where they have to operate efficiently for a continuously varying feed flow rate and composition. The most important catalytic production processes are the following: • • • • • • • • •

the Haber process for ammonia synthesis steam reforming of hydrocarbons to produce synthesis gas methanol synthesis Fischer–Tropsch synthesis hydrogenation/dehydrogenation of organic compounds, sulfuric acid production nitric acid production maleic anhydride production petroleum refining and processing In the area of environmental application of catalysis, the most important processes are

• • • • •

catalytic reduction of NOx catalytic oxidation of SO2 catalytic oxidation of CO, VOC, and hydrocarbons catalytic denitrification of drinking water catalytic oxidation of persistent organic pollutants in wastewater

52


The main advantages and disadvantages of catalysts are Advantages • • •

high efficiency in the treatment of gas emissions large variety of catalysts simple installation

Disadvantages • • • •

deactivation phenomena (mainly in the wastewater treatment) spent catalysts have to be appropriately disposed a specific temperature is required for their operation for low concentrations of VOCs, the heating of large volumes of emissions to the temperature required for catalytic activity is expensive

A look into three-way catalysis The need for controlling the exhaust emissions from automotive vehicles has been recognized since 1975. The most effective and tested method proved to be the installation of three-way catalysts at the exhaust emission system of cars. The development and the improvement of such catalysts was and will be a complicated effort, since a catalyst placed in a vehicle should simultaneously accelerate oxidation and reduction reactions, under continuously changing conditions of temperature and space velocity, in contrast to industrial applications where catalysts operate under fixed and controlled conditions. Generally, the catalytic converter of a vehicle has to satisfy the following requirements: •

• • • • •

facilitate the oxidation reactions of carbon monoxide and unburned hydrocarbons and the reduction of nitrogen oxides (three reactions to perform; hence they are named “three-way catalysts”) start its operation at the lowest possible temperature, since the emissions are high during the first minutes of engine operation, where the temperature is still low show resistance for a short time at temperatures up to 1000 °C exhibit a satisfactory operation for at least 150,000 km be highly active in order to achieve the desired conversions for high volumetric feed of emissions that take place at the engine exhaust all the above have to take place at continuously changing air-to-fuel ratios.

The presence of a three-way catalyst is mandatory for every car produced in the United States and Europe since 1981 and 1993, respectively. It is the most massive and one of the most successful stories in the history of catalysis. The demand for occupying less space, operation at high volumetric feed, and low loss of power led to the adoption of monoliths for the automobile catalyst. A monolith


53

100 90

NOx

80 % conversion

70 60 50 40

HC

30

CO

20 10 0 13.5

14

14.5

15

15.5

air to fuel ratio

Figure 2.3 2000).

The activity of catalytic converter in relation with air to fuel ratio (Shelef and McCabe,

is a ceramic support structured in many channels and shapes that achieve large catalytic surface at small volume. The search for the appropriate active catalytic components ended with the use of Pt, Pd, and Rh. These metals proved to have the required activity, durability at high temperatures, and sufficient resistance to poisoning from the lead traces present in fuels. However, it was the development of electronics and the installation of the so-called “lambda” sensor that allowed the sound operation of catalytic converters by adjusting the air-to-fuel ratio at a specific range of values (Figure 2.3). As mentioned earlier, the oxidation of carbon monoxide and hydrocarbons should be achieved simultaneously with the reduction of nitrogen oxides. However, the first reaction needs oxygen in excess, whereas the second one needs a mixture (fuel-oxygen) rich in fuel. The solution was found with the development of an oxygen sensor placed at exhaust emissions, which would set the air-to-fuel ratio at the desired value in real time. So, the combination of electronics and catalysis and the progress in these fields led to better control of the exhaust emissions from automotive vehicles. A special application: The Earth itself takes advantage of catalytic processes. It seems that catalysis plays a very important role in the global chemistry of Earth atmosphere. Photocatalytic processes may occur in the troposphere on aerosol particles containing Fe2O3, TiO2, and ZnO under the action of the near-ultraviolet, visible, and near-infrared solar light. Photocatalysis is anticipated to affect the intensity of acid rains, the concentration of some greenhouse gases, and free the atmosphere from harmful compounds. Thus, desert areas where continental dust is generated may, perhaps, serve as “kidneys” for the Earth (Zamaraev, 1997).

54


Catalysis may be of interest even on Mars. The Martian atmosphere consists of 95% carbon dioxide and Breedlove et al. (2001) have presented that nickel cluster catalysts could be used in a photoelectrochemical process to split carbon dioxide, according to the reaction v CO2 h → CO ⫹1Ⲑ 2O2

to provide both oxygen to support life systems and carbon monoxide, which can be used as a substitute for hydrogen fuel, in a manned mission to Mars.

MULTIPLE CHOICE QUESTIONS 1.

A value of 3 kcal/mol refers to (a) physical sorption (b) chemical sorption (c) electrostatic sorption

2.

Adsorption of a molecule is the result of (a) the competitiveness between repulsion and the van der Waals force (b) the attraction of the molecule to the adsorbent’s surface (c) the repulsion between the cloud of electrons in atoms that form the surface and those of the molecule

3.

The most important property of an adsorbent is (a) its shape (b) the shape of its pores (c) the number of its pores (d) its pore structure

4.

A pore with a diameter of 15 nm is characterized as a (a) macropore (b) mesopore (c) micropore (d) ultramicropore

5.

Ion exchangers are able to (a) remove organic compounds from a solution (b) release ions when heated (c) take up charged ions from a solution and release an equivalent amount of other ions to the solution

6.

Catalysts can (a) accelerate chemical reactions (b) favor reactions that would not take place otherwise (c) increase the yield of a reaction by changing the equilibrium composition (d) alter both the rate and the thermodynamics of a reaction


55

7.

A catalyst changes the selectivity of a chemical process by (a) altering the thermodynamics (b) affecting each reaction to a different extent (c) changing the equilibrium position

8.

In heterogeneous catalysis, the catalyst provides an alternative pathway of lower activation energy generally through (a) weakening the chemical bonds of the reactants on its surface (b) forming an unstable intermediate with the reactants (c) none of the above

9.

The terms of “catalyst” and “adsorption” were used for the first time respectively, by (a) Keyser and Berzelius (b) Lowitz and Berzelius (c) McBain and Ostwald

10. Adsorption and ion exchange can be (a) considered as two totally different processes (b) described in a mathematically exact manner (c) grouped together as sorption for a unified treatment in practical applications 11. Catalysis by clays is (a) possible for temperatures up to 200°C (b) possible in the hydromethylation of toluene (c) preferable to catalysis by resins in aqueous systems 12. For decolorizing petroleum fractions by adsorption, you would suggest the use of (a) zeolites (b) alumina (c) activated carbons 13. In the adsorption process, there is generally (a) a risk of bed fires in the VOC abatement (b) low capital cost (c) complex installation and maintenance procedure 14. Carbon adsorption is preferably used in water treatment (a) in the case of removing organics with concentrations around 15,000 mg/L (b) if materials being removed are mostly metals (c) for removing ketones 15. If we wanted to remove VOCs from a gas stream containing moisture using the adsorption process, we would suggest the use of (a) zeolites (b) activated carbon (c) a polar adsorbent 16. Ion exchange can be used (a) at high temperatures (b) without prefiltration of the feed to be treated (c) for the treatment of nuclear waste solutions

56


17. Catalysts are appropriate (a) for industrial processes (b) for VOC abatement from stationary sources (c) for both industrial production and environmental applications 18. For the efficient operation of a catalytic converter placed in a car, it is important to adjust the air-to-fuel ratio (a) according to the current engine operating conditions (b) around a specific value (c) so that oxygen excess is achieved 19. Monoliths are used in catalytic converters of automobiles mainly because (a) they provide large surface at small volume and low loss of power (b) of their low cost (c) of their activity 20. Catalytic oxidation is preferable to adsorption, in VOC abatement, if (a) the temperature is below 150°F (b) intermittent loads are involved (c) high concentrations of gaseous pollutants have to be dealt with

ANSWERS TO MULTIPLE CHOICE QUESTIONS 1. 2. 3. 4. 5.

(a) (a) (d) (b) (c)

6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

(a) (b) (a) (a) (c) (a) (c) (a) (b) (b) (c) (c) (b) (a) (c)

physical sorption the competitiveness between repulsion and the van der Waals force its pore structure mesopore take up charged ions from a solution and release an equivalent amount of other ions to the solution accelerate chemical reactions affecting each reaction to a different extent weakening the chemical bonds of the reactants on its surface Keyser and Berzelius grouped together as sorption for a unified treatment in practical applications possible for temperatures up to 200 °C activated carbons a risk of bed fires in the VOC abatement if materials being removed are mostly metals activated carbon for the treatment of nuclear waste solutions for both industrial production and environmental applications around a specific value they provide large surface at small volume and low loss of power high concentrations of gaseous pollutants have to be dealt with

–3– Heterogeneous Processes and Reactor Analysis

3.1 INTRODUCTION TO HETEROGENEOUS PROCESSES In the relevant literature, many definitions of reaction rates can be found, especially in the case of catalytic systems. Depending on the approach followed, a catalytic reaction rate can be based on catalyst volume, surface, or mass. Moreover, in practical applications, rates are often expressed per volume of reactor. Each definition leads to different manipulations and special attention is required when switching from one expression to another. In the following, the various forms of catalytic reaction rates and their connection is going to be presented. Starting from the fundamental rate defined per active site, the reader is taken step –by step to the rate based on the volume of the reactor and the concept of the overall rate in two- and three-phase systems. The analysis in this chapter mainly concerns catalytic reactions. However, the basic principles are applicable to any heterogeneous process, though with different terminology and levels of importance. Concerning adsorption and ion exchange, only the reaction rate per unit mass of solid phase (rm) and per unit volume of reactor (R) are used in practice, whereas the concepts analyzed in the overall rate and rate-controlling sections are equally applicable to ion exchange and adsorption. 3.1.1 Reaction rate in heterogeneous catalysis: from active sites to reactor level Fundamental—Active site level As mentioned in Chapter 2, a catalytic reaction is not catalyzed over the entire surface of the catalyst but only at certain active sites (Fogler, 1999). Then, the reaction rate of any reaction component i at a fundamental level for catalytic reactions can be defined with respect to active sites as follows: 1 dN (3.1) rt n dt

57

58


where: N the moles of reactant that appear in the reaction t time n the number of active sites on the catalyst surface. This rate is usually referred to as the turnover frequency and it is the number of molecules reacting per active site per unit time at the conditions of the experiment (Boudart, 1985; McNaught and Wilkinson, 1997; Fogler, 1999). Boudart (1995) used the term “turnover frequency” to define the number of revolutions of the catalytic cycle per unit time and active site. In each revolution, one mole of reactant is consumed. For example, the revolution of a catalytic cycle for SO2 oxidation is shown in Figure 3.1. Frequently, the number of active sites is expressed in mole units (the number of active sites divided by the Avogadro number) and thus, turnover frequency is found in s-1 units. For a specific reaction, the turnover frequency depends on the nature of the catalytic active site, the temperature, and the reactants’ concentration. The above-defined catalytic rate could be described as an “active-site level” rate. Following the reaction rate definition of the form given in eq. (3.1), if component i is a reaction product the rate is positive; if it is a reactant that is being consumed, the rate is negative; thus, the rate of disappearance of the reactant is –rt. In environmental applications, as we are interested in the disappearance of a pollutant, the rate is expressed as –r, which is positive. The rate of disappearance is used in Chapters 3 and 5, where for simplicity it is referred to as the reaction rate. Catalyst level—active site plus support The rate of a catalytic reaction as defined above exhibits a great disadvantage: the number of the active sites is unknown and cannot be easily determined from common experiments. The difficulties associated to the measurement of active sites leads, for the time being, to the use of “catalyst level rates,” in most practical applications. Specifically, the most common reaction rate types used are expressed per unit volume of the solid phase (rvs), per unit surface of the solid surface (rs) or per unit mass of the solid

½ O2

metal oxidation

SO3

metal reduction

SO2

Figure 3.1 Revolution of a catalytic cycle for SO2 oxidation.

3.1 Introduction to Heterogeneous Processes

59

phase (rm), defined as follows (Levenspiel, 1972): rvs

1 dN VS dt

(3.2)

rs

1 dN S dt

(3.3)

1 dN Ms dt

(3.4)

rm

where: VS the volume of solid phase S the surface of the solid phase MS the mass of the solid phase. For a specific reaction, the reaction rates, as defined in eqs. (3.2)–(3.4), depend on the nature of the catalytic active site, the surface arrangement of the catalyst, the temperature, and the reactants concentration. Surface arrangement here denotes the macroscopic and measurable catalyst basic properties: • •

the specific area of the catalyst Ss S M s the number of catalytic active sites per unit area n S

These two basic properties of the catalyst n M can be related as follows: s n n S n Ss S Ms Ms S

(3.5)

The number of active sites per unit mass of catalyst n M can be referred to as “active s sites concentration.” The principal difference between these “catalyst level” reaction rates and the turnover frequency is that the latter does not depend on the surface arrangement of the catalyst, or in more practical terms, does not depend on the specific physicochemical characteristics of the catalyst as a composite of the active catalytic reagent plus the support. The turnover frequency and the catalyst level reaction rates can be related through the following equalities: M V S 1 dN rt S rvs rs S rm n n n dt n

(3.6)

It is noteworthy that the form of the rate (ri f (state of the system) does not actually depend on our choice of reaction rate definition. Only the rate coefficients and their dimensions change with each rate definition (Levenspiel, 1972).

60


A look into the rate coefficient Following Carberry (1976), in this book the term “rate coefficient” is used for the proportionality coefficients ki in the typical rate expression of the form: ri ki f (C). To simplify the following analysis, a first-order elementary reaction is considered. Then the intrinsic reaction rate can be expressed as rm km Cs

(3.7)

rvs kvs Cs

(3.8)

rs ks Cs

(3.9)

where: CS the surface concentration of the reactant, (moles/m3)of fluid. The several rate coefficients have the following units: 1 m 3 fluid km s kg solid 1 m 3 fluid 1 kvs s m 3 solid s m 1 m 3 fluid ks s s m 2 solid surface Then, after some rearrangements:

M 1 dN 1  MS  S = rt =   k vs C S = k s C S = S k m C S n dt ρp  n  n n kt kt k

(3.10)

t

where, p is the particle density. The parameter kt has the following units: 1 m 3 fluid kt s n This rate coefficient corresponds to the reaction rate rt, namely, to the reaction rate defined per active site. This rate coefficient for a specific reaction and a fixed catalytic agent and temperature can be considered constant. In the case of a porous catalyst, where the internal area contributes the most to the total area, Ss can be considered to be independent from the catalyst shape and size. Furthermore, the number of catalytic active sites per unit area nS can be considered a fixed property for a given catalyst. Consequently, the active sites concentration can nMs be also be


61

considered a given property for a specific catalyst. Then, if the rate coefficient kt is constant, the rate coefficients km, kvs and ks are constants too. The previously reported relationship (eq. (3.10)) manifests one more important characteristic of the catalyst level rate coefficients—for the same reaction, temperature, catalytic agent, and support but for different surface arrangement, i.e. active site concentration, these coefficients will be different; and this is an advantage of the usage of kt, and in general, of turnover frequency (active site level reaction description). In the case of non-porous spherical particles, S Sex dp2 MS pVs p

(3.11)

dp3

(3.12)

6

where Sex denotes the external surface area. Thus, Ss

Sex 6 M S p dp

(3.13)

Then S n n 6 n ex Ms Ms Sex p dp Sex

(3.14)

This result means that the number of active sites per unit mass of catalyst is not constant since it depends on the particle size. Again, the term nS can be considered to be a conex stant property for a given catalyst, prepared by the same technique, for all catalyst sizes. Then S M 1 dN 1 M  rt  S  kvs CS ex kS CS S km CS   n dt n n n p k k k t t t

(3.15)

Then, if the rate coefficient kt is constant, ks is constant too, whereas km and kvs are not constants and are dependent on the particle size of the catalyst (eq. 3.14). The anatomy of rate coefficient In reactions where the rate is expressed as ri ki f (C), the rate coefficient will often depend on the concentrations, because the latter expression does not take into account the interactions between molecules in a reaction mixture that is thermodynamically nonideal (Froment and Bishoff, 1990). In such a case, if the concentrations are substituted by activities, the rate coefficient is merely independent of the concentration of the reacting species, but one should keep in mind that it is still not truly a constant (Fogler, 1999).

62


The rate coefficient is almost always dependent on temperature. However, it can be influenced by total pressure, in both gas and liquid systems, plus ionic strength and solvent in liquid systems. Following Fogler (1999), in the present book, the rate coefficient is considered to be a function of only temperature, assuming that the effect of other variables is much less. Reactor level—Catalyst plus reactor arrangement The principal difference between homogeneous and heterogeneous reaction rates is that the latter is based on mass, volume, or more rarely, on the area of the solid and not on the fluidphase volume or reactor volume. The reactor volume or liquid-phase volume is of secondary significance in heterogeneous reactions since the reaction takes place on the solid rather than throughout the reactor volume. Moreover, the mass of the solid is usually used instead of the solid volume or surface, because it is the most easily measured property. However, for purposes of mass balance in reactors, the following rates have to be also considered: the rate of reaction per unit volume of the fluid phase (ru) and per unit volume of reactor (R), defined as follows (Levenspiel, 1972): ru

1 dN VL dt

(3.16)

R

1 dN VR dt

(3.17)

where: VL the fluid volume VR the reactor volume. So, rm, rvs, rs, and rt are the appropriate rates for expressing the intrinsic catalytic reaction rate, whereas ru and R are phenomenological rates, used for reactor design. More specifically, ru is also called the “pseudo-homogeneous rate” (Schmidt, 2005). For these rates, the following is valid: ru

M 1 dN VS S rvs rs S rm VL VL VL dt VL

(3.18)

The overall rate of reaction (R) per unit volume of the reactor is (Levenspiel, 1972) R

M V 1 dN VS S rs L ru S rm rvs VR VR VR VR dt VR

(3.19)

The design of a reactor is connected to certain preferred parameters and it is useful to know how they are related to each other. For instance, it is very important to use the appropriate terms in order to correlate the reactor volume to the fluid and solid volumes. In Table 3.1, the most important ratios per reactor are presented. VR denotes the total volume of the reactor, VS denotes the volume of the solid, and VL is the fluid volume in two-phase systems and the liquid-volume in three phase systems.


63

Table 3.1 The most important ratios per reactora Reaction rateb

Reactor

Subtype

Fixed bed

Two-phase and Trickle bed

Slurry

Bubble ru Columns and Agitated Vesselsc

Bubble Fluidized phase beds (Two-phase Emulsion model) phase Fluidized beds (L-K model)

All phases

For all – Reactors

R

R R

Vs VL

Vs VR

VL VR

Other

1

1–

Ms b VR

hs

hL

bubb

bub

(1 – bub) (1 – fm)

(1 – bub) fm –

–

–

ms p b

1 fm fm

ru

i

–

–

–

–

–

–

MS ms VL –

–

MS p VS

is the fixed-bed porosity (voidage), b is the bulk density of solids, p is the particle density, ms is the mass of solid per unit volume of bubble-free liquid in slurry reactors, hS and hL are the fractional solid and liquid holdup in slurry reactors, i is the volume of a specific phase per unit volume of the fluid bubbles phase in fluidized beds, bub and fm are the fraction of the bed occupied by fluid bubbles and the bed voidage at minimum fluidization state in fluidized beds, respectively. b The reaction rate that is most commonly used in the analysis of the corresponding reactor type or model. c Three- (slurry) and two-phase systems. a

Another critical issue that definitely needs to be clarified is the surface of the catalyst per unit volume of reactor or fluid, which is used in reactor analysis. The total surface area of a catalyst includes the internal and the external surface areas. Thus For fixed-beds: • for porous particles, • for nonporous particles,

S M S s s S s b VR VR

(3.20)

S ex u VR

(3.21)

64


For slurries: • for porous particles, SM S s s Ss ms VL VL

(3.22)

S ex c VL

(3.23)

S M S s s S s p VS VS

(3.24)

Sex 6 VS dp

(3.25)

• for nonporous particles,

For particles: •

•

for porous particles,

for nonporous particles,

where: Ss the surface area per unit mass of catalyst Sex the external surface area of catalyst. By using these ratios and the relationships (3.18) and (3.19), we can alter the material balance expressions and the corresponding solutions of the reactor models if we use other rate expressions. It should be noted that in practice, in fixed-beds and slurries of porous particles, the external area of the particle and thus the parameters au and ac are used, respectively. It should be noted that when referring to the reaction rate per unit surface of a porous particle or to the corresponding rate coefficient, it should be clarified whether the rate or the rate coefficient is based on the external or the total surface area. Then, for example, in a slurry reactor where the reaction rate is expressed per unit volume of liquid, the rate could be ru Ss ms ks f (C )

(3.26)

ru ac ks f (C )

(3.27)

or

These two expressions are equivalent only for nonporous particles. For porous particles, Ss⋅ms is very different from ac as well as the corresponding ks values.


65

A look into turnover frequency Beyond the difficulties met in the determination of the number of active sites in order to estimate the turnover frequency, there are more problems to be solved. The turnover frequency, even in the case that it is measured by employing an ideal experiment, might be only an average one. This is partly because the active sites could exhibit different reactivities, i.e. they are not identical in respect to the catalytic reaction. An interesting question, expressed by Boudart (1985), is the following: As particle size grows from that of a small cluster to infinite value for a single macroscopic crystal, how does the value of turnover frequency change for a given reaction on a given metal? Several experimental results revealed that turnover frequency could be different for a class of reactions called “structure-sensitive” (Boudart, 1981; Yates, 1981). An operational definition of structure sensitivity (Bouldart, 1995) is that “the turnover frequency depends on surface crystalline anisotropy revealed by working on different faces of a single crystal or on clusters of varying size between 1 and 10 nm.” The surface crystalline anisotropy is directly related to the crystallographic orientation (planes) of the supported catalytically active atoms, which in turn, is affected by the particle size (Bouldart, 1981). It should be noted that the term “particle” here has the meaning of a pure active agent structure. Thus, by increasing particle size, the number of active sites is increased too. In contrast, structure-insensitive reactions are those for which turnover frequency under fixed conditions does not depend or depends slightly on the surface crystalline anisotropy of clusters of varying size or of single crystals exposing different faces. For these kinds of reactions, all accessible surface atoms can be considered as equally active sites (Boudart, 1981 and 1995). In order to avoid any confusion, the “surface structure” used in sensitive and insensitive reaction analysis has nothing to do with the “surface arrangement” used in the catalyst level rates analysis—the first refers to the microscopic level of the active site, whereas the latter to the catalyst level. The usage of turnover frequency exhibits various advantages (Budart, 1995): •

•

The value of the turnover frequency can be reproduced in different laboratories, if the method of measurement of the rate and the counting of sites are kept the same. Moreover, the use of turnover frequency allows the comparison between two catalysts that differ in metal or size for a specific reaction. The great advantage of such a comparison is that the activity of different catalysts is compared at active site level without the considerations of catalyst arrangement. To be more specific, using turnover frequency, we can compare the activity of the pure active site, ignoring the specific area of the catalyst. By definition, the turnover frequency is expressed per number of active sites. So, catalytic samples that differ only in the amount active sites must exhibit the same values of turnover frequency. If not, heat and mass transfer phenomena are present. Specifically, the correct measurement of intrinsic kinetic data in heterogeneous catalysis is difficult due to the effect of heat and mass transfer, especially inside the pores of high specific-area materials. The turnover frequency reveals these phenomena. In other words, in the case of supported

66


metals, if the same value of turnover frequency is obtained for a specific reaction at fixed conditions on two catalytic samples containing different amounts of metal on the porous support, the kinetic data are not obscured by heat or mass transfer phenomena. 3.1.2 The concept of the overall reaction rate in heterogeneous reactions General In heterogeneous reactions, phase boundaries exist between phases and transport processes; the intrinsic rate of reaction should be taken into account simultaneously in reactor design. The combination of mass transfer rates and reaction rates leads to the socalled overall rate. The goal is to express the global rate in terms of the bulk properties of the phases, eliminating the interphase properties. If the overall phenomenon requires that a number of steps take place in series, then, at steady state, all these steps will proceed at the same rate, which is equal to the overall rate (Levenspiel, 1972): roverall r1 r2 ... rn

(3.28)

There are cases, as in catalysis, where some steps are in parallel. In these systems, the overall rate is greater than the rate of each individual step. If these steps are independent of each other, the overall rate is the sum of all individual rates (Levenspiel, 1972). n

roverall ∑ ri

(3.29)

n1

The elimination of the interphase concentrations could be done easily if the rate expressions of all steps are linear in concentration. However, for nonlinear expressions, it is difficult to evaluate and handle the overall rate. We will examine some simple cases in twoand three-phase systems. Two-phase systems In the case of two fluids, two films are developed, one for each fluid, and the corresponding mass-transfer coefficients are determined (Figure 3.2). In a fluid–solid system, there is only one film; whereas the resistance within the solid phase is expressed by the solid-phase diffusion coefficient, however, in many cases an “effective” mass-transfer coefficient is used in the case of solids as well. Consider the irreversible catalytic reaction of the form A(g)B(g) Here, we consider the general case of a porous catalyst, where the internal diffusion effect is included in the effectiveness factor (s). The intrinsic rate of reaction per unit mass of catalyst is (in mol/m2s) (rs )

1 dN s ksCs S dt

(3.30)


67

C

R

δ

Figure 3.2 Two-phase system.

This is the rate of disappearance of the reactant A and S the total surface of the catalyst, m2 ks the rate coefficient, m3/m2s Cs the concentration of reactant at the outer surface of the catalyst particle, mol/m3. The rate of mass transfer from the bulk of the gas to the catalyst surface is: rg k g (C G Cs )

(3.31)

where: kg the mass transfer coefficient in the gas film, m/s CG the concentration of reactant in the bulk gas phase, mol/m3. At steady state, (rs ) rg

1 dN s ksCs k g (CG Cs ) S dt

(3.32)

From the equality of the rates, the concentration of reactant at the outer surface of the catalyst particle can be expressed in terms of bulk concentration: Cs

kg k g s ks

CG

(3.33)

Then, the overall reaction rate (rov) can be also expressed in terms of the bulk concentration of the reactant: rov (rs ) rg s ks where kov is the overall coefficient in m/s.

kg k g s ks

CG k ovC G

(3.34)

68


The reciprocal of this coefficient is referred to as total resistance: 1 1 1 k ov k g s ks

(3.35)

It should be noted that the overall coefficient (kov) has been derived under the assumption that the reaction is taking place mainly in the internal surface of the catalyst. See Chapter 5 for a more rigorous analysis on the subject. The concept of the rate-controlling step When a process consists of several individual steps in series, the concept of the ratecontrolling step simply states that one of the several steps involved provides the major resistance to the overall process. In such a case, this slow step is termed the “ratecontrolling step” and can be considered as acting alone (Levenspiel, 1972). Instead of molecules flowing, consider water flowing through a pipe with a number of partially opened valves. The flow of the water in the pipe will be determined by the valve that offers the largest resistance to the flow. Actually, we could come up with a fairly accurate estimate of the flow by calculating the resistance to the flow in this valve, neglecting all the others. Consider the first-order reaction analyzed in the previous paragraph. In the limiting case where ks → ∞ or ks kg, the resistance to the overall rate is due to the gas film around the catalyst and Cs → 0. The rate-controlling step is the diffusion in the gas film and the overall rate is (3.36) (rs ) k gC G On the other hand, if kg → ∞ or kg ks, the resistance to the overall rate is owing to the intrinsic reaction rate and CG → C s. The rate-controlling step is the reaction rate and the overall rate is (rs ) s ksCG

(3.37)

Note that due to the equality of the individual rates, if ks → ∞ then Cs → 0, and if kg → ∞ then (CG – Cs) → 0 or CG → Cs; and so, the individual rates are finite and equal to the overall rate. It is the resistance of the individual step and not the corresponding rate that could be zero under certain operating conditions. The concept of the rate-controlling step is much more useful in complex kinetic expressions, where the overall rate is nonlinear and cannot be obtained by following a simple procedure as presented above for the case of a first-order reaction. For example, consider a second order reaction. In this case, the intrinsic reaction rate is (rs ) ksCs2

(3.38)

while the rate of mass transfer from the bulk of the gas to the catalyst surface is rg kg (CG Cs )

(3.39)


69

Then (rs ) rg ksCS2 kg (CG Cs )

(3.40)

Working as in the first-order reaction case, it can be proved that after the elimination of the surface concentration we have Cs

kg kg2 4 kg ksCG

(3.41)

2 ks

It is obvious that the reaction rate becomes a complicated expression with the introduction of the surface concentration. rov (rs ) rg

kg 2 ks

2 ks C G k g k g 4 k g k s C G

(3.42)

In this case, we cannot work as in the case of the first-order reaction to derive more simple expressions. However, the principle of the rate-controlling step is still applicable. If the rate-controlling step is the diffusion in the gas film, the overall rate (rov), is rov kgCG

(3.43)

If the rate-controlling step is the reaction rate, the overall rate (rov), including the effectiveness factor, is rov s ksCG2

(3.44)

Three-phase systems In three-phase systems, two interfaces exist, i.e. the gas bubble–liquid interface and the liquid–solid interface and thus, four mass-transfer steps and the corresponding films are involved in the process (Figure 3.3) • • • •

mass transfer from the bulk gas to the gas bubble–liquid interface (gas-bubble film) mass transfer from the bubble interface to the bulk liquid (liquid film around the bubble) mass transfer to the solid surface (liquid film around the particle) mass transfer within the solid phase

All these mechanisms along with any reaction in the solid phase are considered to be processes in series (Smith, 1981). In three-phase systems, three interface concentrations, two in the gas–liquid interface CG,i and CL,i, and one in the liquid–solid interface Cs, have to be eliminated. If equilibrium exists at the bubble–liquid interface, CG,i and CL,i are related by Henry’s law: CG,i HCL,i

(3.45)

70


gas-liquid interface

CG,i CL,i CS

CG bulk gas CL

solid bulk liquid

gas film liquid film

liquid film

Figure 3.3 Mass transfer in three-phase systems.

where H is the dimensionless Henry’s constant. Similar to the two-phase case, for a first-order reaction (in mol/m2s), (rs ) ks sCS

(reaction on the solid surface)

rf kf (CL CS ) (bulk liquid to catalyst surface)

(3.46) (3.47)

where: kf the liquid–solid mass transfer coefficient, m/s CL the liquid-phase bulk concentration. In three-phase systems, two more films exist and the corresponding mass transfer rates are rg kg (CG CG,i ) (bulk gas to bubble interface)

(3.48)

rfg kfg (CL,i CL ) (bubble interface to bulk liquid)

(3.49)

where kfg is the gas–liquid interface liquid mass transfer coefficient, m/s. At steady state, rov (rs ) rf rg rfg

(3.50)

Then, the above equations can be combined appropriately to eliminate the unknown concentrations and the rate can be expressed solely in terms of the concentration of the reactant in the bulk of the gas: rov K ovCG

(3.51)


71

where Kov is an overall coefficient (in m/s): 1 1 1 H 1  H  K ov kg kfg  kf s ks 

(3.52)

It is noteworthy that not all the resistances are significant in every case. If only a pure gas constitutes the gas phase and for slightly soluble gases, the resistance to the mass transfer on the liquid side of the interface is predominant. Under these conditions, CG CG,i and the above equation reduces to  1 1 1 1  H  K ov  kfg kf s ks 

(3.53)

Even when the gaseous reactant is in a mixture with other components in the bubbles, kg appears to be much larger than kfg /H and thus, the last equation is applicable. Derivation of an overall gas transfer rate In many three-phase systems, the two resistances in the gas–liquid interface are combined in one overall gas mass transfer coefficient KL. To do this, we combine the following rates: rg kg (CG CG,i ) (bulk gas to bubble interface)

(3.54)

rfg kfg (CL,i CL ) (bubble interface to bulk liquid)

(3.55)

If equilibrium exists at the bubble–liquid interface, CG,i and CL,i are related by Henry’s law: CG,i HCL,i

(3.56)

Then CL,i

kgCG kfgCL kg kfg H

(3.57)

The rate becomes rfg kfg (CL,i CL )

Hkg kfg  CG  CL     kfg Hkg H

(3.58)

Defining an overall gas-phase mass transfer coefficient KL (in m/s), KL

Hkg kfg kfg Hkg

1 1 1 K L Hkg kfg

(3.59)

72


we have rfg rg K L (CL,eq CL )

(3.60)

where CL,eq is the equilibrium concentration in the liquid: CL,eq

CG H

(3.61)

This is the concentration in equilibrium with the bulk gas concentration CG. It is important to note that in the general case, CL,eq CL,i

(3.62)

CG CG,i

(3.63)

CG (kfg kg )CL kfg H kg

(3.64)

Note that CL,i

CG,i CL,i H

(3.65)

In the case of kg → ∞, or in other words, when there is no resistance in the gas phase (gas phase consists of a pure gas), CL,i

CG CL,eq H

(3.66)

CG,i CG

(3.67)

K L kfg

(3.68)

Finally, if the liquid is saturated with gas, CL,eq CL

(3.69)

3.2 HETEROGENEOUS REACTORS 3.2.1 Introduction Chemical reactors vary widely in shape and in the mode of operation. Consequently, there are various ways of classifying them. The first classification is based on the number of the

3.2 Heterogeneous Reactors

73

involved reacting phases— • •

homogeneous reactors, where one phase such as liquid or gas exists in the reactor heterogeneous reactors, where two or three distinct phases coexist in the reactor.

A further classification of homogeneous and heterogeneous reactors is based on the nature of the involved reacting phases— •

homogeneous reactors • liquid-phase reactors • gas-phase reactors

•

heterogeneous reactors • liquid–solid (L–S) reactors • gas–solid (G–S) reactors • liquid–gas (L–G) reactors • liquid–gas–solid (L–G–S) reactors

Finally, classification could be based on the contacting pattern of the involved reacting phases, as it is described in the following sections. 3.2.2 Homogeneous reactors Plug-flow tubular reactor (PFTR): This reactor is operated under steady-state condition. The reactor is of tubular shape, the reactants enter at the inlet and the composition is a function of the distance from the inlet. However, the composition is not a function of time. The ideal plug-flow reactor is characterized by the absence of mixing in the direction of flow and complete mixing in the transverse direction. Continuous-stirred tank reactor (CSTR): This reactor is operated under steady-state condition. The reactants flow continuously in and out of the vessel at a constant flow rate and are perfectly mixed by mechanical means, and thus the composition is the same throughout the reactor. The result is that the exit concentration is the same as the one in the reactor. The concentration is constant, i.e. is not time-dependent. Batch-stirred tank reactor (BSTR): In this type of reactor, the reactants are fed into the container, they are well mixed by means of mechanical agitation, and left to react for a certain period of time. This is an unsteady-state operation, where composition changes with time. However, the composition at any instant is uniform throughout the reactor. 3.2.3 Heterogeneous reactors Gas–liquid heterogeneous reactors Gas–liquid continuous-stirred tank reactor: This is a CSTR, where the liquid and gas phases are mechanically agitated (Figure 3.4).

74


G

L

L

Figure 3.4 A gas–liquid continuous-stirred tank reactor.

Gas–liquid bubble column: This reactor is of tubular shape (Figure 3.5). The liquid phase is agitated by the bubble rise of the gas phase. The gas phase flows through the reactor upward at a constant rate. The liquid phase is continuous. This reactor could be of continuous type, if the liquid is flowing through the reactor continuously; or semibatch, if the liquid is stationary in the reactor. Two-phase fluid–solid heterogeneous reactors Agitated tank reactors Batch agitated reactor: This is a batch stirred tank reactor. For liquid–solid systems, the liquid is agitated by a mechanical apparatus (impeller) and the reactor is of tank shape. For gas–solid systems, the gas is agitated and rapidly circulated through a fixed-bed of solids. This reactor is basically an experimental one used for adsorption, ion exchange, and catalysis studies. Carberry reactor: The gas flows continuously through the tank-type reactor, whereas the catalyst is placed in rotating baskets in the reactor. This reactor is also experimental and suitable for catalysis as well as for adsorption studies (Figure 3.6). Fixed-bed reactors Fixed-bed reactor (FBR): Gas or liquid reactants flow at a constant rate over a fixed-bed of solids placed in a tubular vessel (Figure 3.7). The fixed-bed reactor can be viewed as a semibatch reactor, where the solid phase is fixed and the fluid is flowing continuously through the reactor. The operation is steady state for catalysis and unsteady state for adsorption and ion exchange. Some applications of FBR are the following: HDS of naphtha, catalytic reforming, steam reforming, water-gas shift, methanation, ammonia synthesis, and methanol synthesis. This contacting pattern is by far the most common in adsorption and ion-exchange processes. Monolith reactor: This type of reactor is used extensively for the abatement of automobiles exhaust emissions. The gas flows continuously through the reactor, whereas the catalyst is a continuous phase consisting of a ceramic support and the active phase, which is dispersed onto the support. The support is structured in many channels and shapes that achieve large catalytic surface at small volume. A typical application of monolith reactors is the exhaust gas cleaning.


75

L

L

G

Figure 3.5 A bubble column reactor.

C

C fluid

Figure 3.6 A Carberry reactor (C is the catalyst).

fluid

fluid

Figure 3.7 The fixed-bed reactor.

Reactors with moving solid phase Moving-bed reactor: There are various installations of moving-bed reactors. In the combustion of large coal, the solid phase is in cross-flow to the air supply by means of a moving strip. In another type of moving-bed reactor, the solid particles are fed at the top and continuously move downward to be discharged at the bottom. Catalytic reforming and coal combustion are typical applications of this type of reactor.

76


Fluidized-bed reactor (FLBR): The up-flow gas or liquid phase suspends the fine solid particles, which remain in the reactor (Figure 3.8). This reactor is of tubular shape with a relatively low aspect ratio of length to diameter. The most common application of FLBR is the classical FCC process. Entrained flow reactor (riser): This is a fluidized-bed reactor in which the solid is entrained by the fluid phase and is recycled throughout the operation (Figure 3.9). Some applications of this reactor type are the modern FCC process and the calcination of alumina hydrate. Three-phase heterogeneous reactors Three-phase reactors are generally needed in cases where there are both volatile and nonvolatile reactants, or when a liquid solvent is necessary with all reactants in the gas-phase (Smith, 1981). Some examples are

solid particles

fluid

Figure 3.8 The fluidized-bed reactor.

gas

Figure 3.9 The entrained flow reactor.


• • • •

77

hydrogenation of oils oxidation of liquids oxidation of pollutants dissolved in liquids polymerization reactions.

Fixed-bed reactors Trickle-flow reactor (TFR): This is a tubular flow reactor with a concurrent down-flow of gas and liquid over a fixed-bed of catalyst (Figure 3.10). Liquid trickles down whereas the gas phase is continuous. This reactor is mainly used in catalytic applications. Typical application examples of this reactor type are the following: HDS of heavy oil fractions and catalytic hydrogenation of aqueous nitrate solutions. Packed Bubble Bed Reactor (BBR): This is a tubular flow reactor with concurrent upflow of gas and liquid (Figure 3.11). The catalyst bed is completely immersed in a continuous liquid flow while gas rises as bubbles. Some applications of BBR are the catalytic denitrification of aqueous nitrate solutions and the hydrogenation processes. Reactors with moving solid phase Three-phase fluidized-bed (ebullated-bed) reactor: Catalyst particles are fluidized by an upward liquid flow, whereas the gas phase rises in a dispersed bubble regime. A typical application of this reactor is the hydrogenation of residues. L G

G L

Figure 3.10 The trickle-bed reactor. L G

L

G

Figure 3.11 The packed bubble bed reactor.

78


Slurry Bubble Column Reactors (SBCR): This reactor is tubular (Figure 3.12). The liquid is agitated by means of dispersed gas bubbles. Gas bubbles provide the momentum to suspend the catalyst particles. The gas phase flows upward through the reactor at a constant rate. This reactor could be of continuous type or of semibatch type. This type is used only in catalysis. Agitated slurry reactor (ASR): This is a mechanically agitated gas–liquid–solid reactor (Figure 3.13). The liquid is agitated by a mechanical apparatus (impeller). The fine solid particles are suspended in the liquid phase by means of agitation. Gas is sparged into the liquid phase, entering at the bottom of the tank, normally just under the impeller. This reactor can also be of continuous type or of semibatch type. This type is used only in catalysis.

3.3 TWO-PHASE AGITATED REACTORS The analysis of this type of reactor requires a uniform composition of fluid phase throughout the volume. While this is easily achieved by standard agitation devices for liquid–solid systems, i.e. impellers, it requires special design to be achieved for gas–solid systems. This type of reactor is basically used for laboratory experimentation.

G L

L G

Figure 3.12 The slurry bubble column reactor.

G G

L

L

Figure 3.13 The agitated slurry reactor.

3.3 Two-Phase Agitated Reactors

79

3.3.1 Mixing in liquid–solid reactors An agitator is a device consisting of at least a power package, a shaft, and an impeller to agitate the contents of a vessel. Mechanical agitation of a liquid by a rotating device such as an impeller is especially suitable for dispersing solids, liquids, or gases into liquids; and is used for a variety of applications where mass transfer is of principal importance. In general, agitators are able to produce highly turbulent flows, which at the same time produce good mass transfer coefficients and effective dispersion of solids, liquids, and gases. The impeller is the part of the agitator that impacts force to the material being mixed. Propellers, turbines, gates, anchors, and paddles are all types of impellers. Typically, the impeller is a single propeller or turbine blade connected to a shaft that is driven by an electric motor at a fixed speed. There are two classes of impeller agitators: axial-flow and radial-flow, and the mixing characteristics are shown in Figure 3.14. Axial-flow impellers generate currents parallel to the axis of the impeller shaft. Radialflow impellers generate currents in a direction tangential or radial to the axis of the impeller shaft. Within the two classes of impellers, there are three main types of impeller design. These are propeller, turbine, and paddle. The three main types are utilized in about 95% of most batch liquid agitation systems. Standard propellers have three blades, but two-bladed, four-bladed, or impellers encased by a circular guard can also be used. Axial-flow and mixed-flow impellers Propeller A propeller is a three- or four-bladed flow impeller, having helically shaped blades. The flow is primarily axial (discharge flow parallel to the agitator shaft) and is most effective in low-viscosity fluids. The marine-type propeller (Figure 3.15) is characteristically operated at relatively high speed, particularly in low-viscosity liquids (Treybal, 1980). A revolving propeller traces out a helix in the fluid. One full revolution moves the liquid a fixed distance. The ratio of this distance to the propeller diameter is known as the pitch. In the case of turbines, “pitch” is the angle the blades make with the horizontal plane. Propellers are members of the axial class of impeller agitators. The propeller is turned so that it produces a flow toward the bottom of the vessel. Propellers are more frequently used for liquid blending operations than for mass transfer purposes (Treybal, 1980).

Figure 3.14 Left: Axial-flow pattern (marine impeller). Right: Radial-flow pattern (flat blade turbine impeller).

80


Figure 3.15 Marine-type (propeller) impeller.

Pitched blade turbine The pitched blade turbine is essentially a modified open flatblade turbine with the blades angled (Figure 3.16). It is available with different blade angles and the 4-blade, 45° pitch turbine is the most common and popular type. The flow is discharged both axially and radially, depending on the angle. For the typical case of a 45° axial-flow turbine, one half of the total flow is discharged axially and one half radially (mixed-flow). The 45° axial-flow turbine is more efficient at all Reynold’s numbers than the flat-blade radial turbine. However, as the pitch angle decreases below 45°, the impeller becomes increasingly sensitive to high viscosity. The pitched-blade turbine is a reasonably cost-effective impeller in both turbulent and laminar flow. It is also a suitable impeller for applications where the viscosity changes over a wide range causing the flow regime to vary between turbulent and laminar flow. Moreover, it is a cost-effective impeller for solid suspensions. Radial-flow impellers and turbines Flat-blade radial-flow impellers The flat-blade radial turbine has vertical blades, parallel to the mixer shaft. Four blades are most commonly used although radials are available with as few as two blades to as many as eight. The flow is discharged radially and splits into two equal flows after leaving the blade tips. Two types of radial turbines are generally in use. They are the open type with blades fastened to the impeller hub (open-type turbines), or the disc type with blades fastened to a disk, which is attached to the hub (Rushton turbines). The turbine diameter is typically 30 – 50% of the vessel diameter. Radial-flow impellers have blades that are parallel to the axis of the drive shaft. The smaller multiblade ones are known as turbines; larger, slower speed impellers with two or four blades are often called paddles. Paddles are two or four blades mounted on the end of the agitator shaft. They are a subset of the radial class of impeller agitators. Typically, the impeller diameter of paddles is 50 –80% of the tank diameter. Turbines, particularly the flat-blade designs, are frequently used for mass transfer operations (Treybal, 1980). Open-type turbines This kind of turbines is also called “full-blade turbines” (Figure 3.17). The blades are vertical (parallel to the axis of the drive shaft) and could be straight or curved. They are cost-effective impellers for operations very near the floor of a tank for agitating the


81

Figure 3.16 Pitched-blade turbine.

Figure 3.17 Left: Straight-blade open turbine. Right: Curved-blade open turbine.

heel in solid suspension applications. Moreover, they are effective in laminar flow applications, especially when impeller Reynolds numbers drop below 50. Disc-type turbines In this type of turbines, blades can be straight or curved. In the related literature, the term “straight” is some times replaced by the term “flat.” The most popular turbine of this type is the straight-blade disc turbine, which is better known as the “Rushton turbine” (Figure 3.18). The same turbine is also called “flat-blade turbine, vaned disc” or simply “flat-blade turbine.” This type is a good cost-effective impeller for low concentrations of immiscible liquid or gas. Two very strong trailing vortices are shed from each blade. These areas of high shear are responsible for breaking the larger droplets to smaller droplets. Maximum aeration numbers should be limited to 0.1. Like all radial-flow impellers, the Rushton turbine is designed to provide the high shear conditions required for breaking bubbles and thus increasing the oxygen transfer rate.

82


Figure 3.18 Rushton impellers.

Vertical blade disc turbines can have “curved” blades, and in this case they are called “Smith” turbines. Flow patterns in agitated vessels Unbaffled tanks If a low-viscosity liquid is stirred in an unbaffled tank by an axially mounted agitator, there is a tendency for a swirling flow pattern to develop regardless of the type of impeller (Perry and Green, 1999). Figure 3.19 shows a typical flow pattern. A vortex is produced owing to the centrifugal force acting on the rotating liquid. In spite of the presence of a vortex, satisfactory process results often can be obtained in an unbaffled vessel. However, there is a limit to the rotational speed that may be used, since once the vortex reaches the impeller, severe air entrainment may occur. The so-called surface aeration is undesirable due to its negative effect on the mass transfer coefficients (see Section 3.5.3). In addition, the swirling mass of liquid often generates an oscillating surge in the tank, which coupled with the deep vortex, may create a large fluctuating force acting on the mixer shaft. The drawing of gas into liquid is frequently undesirable, in addition, vortex formation leads to difficulties in scaling up, so that steps are usually taken to prevent vortices (Treybal, 1980) (Figure 3.20). Baffled tanks In this case, the tank is supplied with baffles that are flat vertical strips placed radially along the tank wall so that adequate agitation of thin suspensions can be achieved, as shown in Figure 3.21. Usually, four baffles are enough. A common baffle width is one-tenth to one-twelfth of the tank diameter (radial dimension). In the agitation of slurries, the accumulation of solids near the walls or baffles has to be avoided. It can be prevented by placing the baffles at a distance that is half their width, from the vessel wall . For Reynolds numbers greater than 2000, baffles are commonly used with turbine impellers and with on-centerline axial-flow impellers. The use of baffles results in a large top-to-bottom circulation without vortexing or severely unbalanced fluid forces on the impeller shaft. In the transition region (10 NRe 10,000), the width of the baffle may be reduced to one-half the standard width. In the case that the circulation pattern is satisfactory in an


83

liquid level

Figure 3.19 Typical flow pattern in an unbaffled tank.

Figure 3.20 Typical flow pattern for a noncentered impeller.

Figure 3.21 Typical flow pattern in a baffled tank.

84


unbaffled tank but a vortex creates problems, partial-length baffles may be used. These are of standard width and extend downward from the surface into about one-third the liquid volume. In the region of laminar flow (NRe < 10), the same power is consumed by the impeller whether baffles are used or not, and they are not usually required. The flow pattern may be affected by the baffles, but not always advantageously. When they are needed, the baffles are usually placed one or two widths radially off the tank wall, to allow fluid to circulate behind them and at the same time produce some axial deflection of flow. 3.3.2 Mixing in gas–solid systems Batch reactors To ensure complete and uniform mixing conditions of the fluid throughout the reactor, in such systems, a special design is required. Such a design has been presented by Levenspiel (1972) and it is shown in Figure 3.22. In this type of reactor, an agitator is used for mixing the fluid in the main body of the vessel, whereas the gas is rapidly circulated throughout the reactor and forced to flow through the catalyst bed. A low conversion per pass through the catalyst is required so that uniform composition in the reactor is achieved. Continuous flow reactors A mixed-flow reactor requires uniform composition of the fluid phase throughout the volume while the fluid is constantly flowing through it. This requires a special design in order to be achieved in the case of gas–solid systems. These reactors are basically experimental devices, which closely approach the ideal flow conditions and have been devised by Carberry (Levenspiel, 1972). This device is called a “basket-type mixed reactor” (Figure 3.6). The catalyst is contained in four rapidly spinning wire baskets.

Rapid circulation of gas

gas

Figure 3.22 Mixing in gas–solid batch reactors.

catalyst


85

This kind of reactor is very useful in experimental studies when the goal is the elimination of the external fluid film resistance. It is used for catalytic as well as adsorption systems (Ruthven, 1984). 3.3.3 Material balances in two-phase agitated reactors Batch reactors For any reactor, the conservation principle can be represented by the following relationship:  rate of flow   rate of flow      of i into    of i out of  volume element  volume element  rate of consumption ()  rate of accumulation       or production () of i   of i within   within volume element   volume element 

(3.70)

In the ideal batch stirred-tank reactor (BSTR), the fluid concentration is uniform and there are no feed or exit streams. Thus, only the last two terms in the previous equation exist. For a volume element of fluid (VL), the mass balance for the limiting reactant becomes (Smith, 1981; Levenspiel, 1972) 1 VL 1 VL

(ru )

dN 1 d (CVL ) dt dt VL d V d C   C L   VL dt dt 

(3.71)

where C is the concentration of a species at any time t, ru is the overall rate of disappearance per unit volume of the fluid phase, and the fractional conversion of the limiting reactant x is defined as (Levenspiel, 1972)

x

Ni Nt Ni

(3.72)

where Ni and Nt are the initial moles and moles at time t, respectively. The limiting reactant is what will run out first during the reaction, i.e. the reactant whose quantity is less than that defined by the stoichiometry of the reaction. Note that the fluid volume (VL) is generally a variable, i.e. a function of time. If the volume of the reaction mixture is constant, eq.(3.71) becomes (ru )

dC dx Ci dt dt

(3.73)

86


where: x

Ci Ct Ci

(3.74)

and ru is the overall rate of reaction (disappearance) per unit volume of the fluid phase. Note that this is identical to the definition of the (homogeneous) reaction rate. This is because the restrictions of uniform concentration and temperature are satisfied in an ideal BSTR and the volume has been assumed to be constant. Since BSTR is used mainly for liquid-phase reactions, the latter assumption usually holds. It should be noted here that while in catalytic systems the rate is based on the moles disappearing from the fluid phase – dC/dt, and the rate has the form (ru) f (k, C), in adsorption and ion exchange the rate is normally based on the moles accumulated in the solid phase and the rate is expressed per unit mass of the solid phase dq/dt where q is in moles per unit mass of the solid phase (solid loading). Then, the rate is expressed in the form of a partial differential diffusion equation. For spherical particles, mass transport can be described by a diffusion equation, written in spherical coordinates r: rm

 2 q 2 q  dq Ds  2 dt r r   t

(3.75)

where Ds is the solid diffusion coefficient and q is the solid-phase concentration of the solute. Finally, the rate of change of a species is related to the stoichiometry. For the general reaction of the form aA bB cC dD the rates are (Fogler, 1999) 1 1 1 1 rA rB rC rD a b c d

(3.76)

Continuous flow reactors In the ideal CSTR, the fluid concentration is uniform and the fluid flows in and out of the reactor. Under the steady state condition, the accumulation term in the general material balance, eq. (3.70), is zero. Furthermore, the exit concentration is equal to the concentration in the reactor. For a volume element of fluid (VL), the mass balance for the limiting reactant becomes (Levenspiel, 1972) Fi Fo (ru )VL 0

(3.77)

where F is the molar feed rate of the limiting reactant. Subscripts i and o denote the inlet and outlet parameters, respectively. In analogy to the batch reactor, x

Fi Fo Fi

(3.78)


87

then Fi xA (ru )VL

(3.79)

Variable-volume systems A variable-volume batch reactor is a constant-pressure (piston-like) closed tank. On the other hand, a variable-pressure tank is a constant-volume batch reactor (Fogler, 1999). Thus, in batch reactors, the expansion factor is used only in the case of a constant-pressure tank whereas and not in a constant-volume tank, even if the reaction is realized with a change in the total moles. However, in continuous-flow reactors, the expansion factor should be always considered. In the following section and for the continuous-flow reactors, the volume V can be replaced by the volumetric flow rate Q, and the moles N by the molar flow rate F in all equations. Change in the total moles in gas–solid reactions

Consider a reaction of the form

aA bBcC dD where all the reactants and products are gases and A is the limiting reactant. Then, based on the conversion level of A, initial moles, N tot,i N A,i N B,i N C,i N D,i

(3.80)

final moles after a conversion level x of A, N A N A,i xA N A,i

(3.81)

b N B N B,i xA N A,i a

(3.82)

c N C N C,i xA N A,i a

(3.83)

d N D N D,i xA N A,i a

(3.84)

N tot N tot,i xA N A,i

(3.85)

and thus,

where xA is the conversion of the limiting reactant A, xA

N A,i N A,o N A,i

(3.86)

88


and is defined as

cd ab a

(3.87)

This parameter can be termed as “fractional mole change.” Note that in the calculation of , inerts that are involved in the gas phase are not taken into account. They are taken into account only in the determination of the total moles of the reacting system. The expansion factor R is defined as (Fogler, 1999) R

N tot,( x1) N tot,i N tot,i

N A,i N tot,i

(3.88)

Then N tot 1 R xA N tot,i

(3.89)

Since N

PV ZRT

(3.90)

where Z is the compressibility factor. In practice, the compressibility factor does not change significantly during the course of reaction, and thus Z ≈ Zi. Then, the volume change in the case of nonisothermal and nonisobaric operation is (Fogler, 1999) P T V (1 R xA ) i Vi P Ti

(3.91)

For a constant volume container (batch reactor), V Vi and thus, eq. (3.91) can be used to calculate the pressure inside the reactor as a function of temperature and conversion. Under constant P and T, eq. (3.91) becomes V 1 R xA Vi

(3.92)

In this relationship, Vi is the initial (feed) volume of the gas. This is the case of Levenspiel’s simplification where the volume of the reacting system varies linearly with conversion (Levenspiel, 1972). The last equation shows that even if we have a change in moles (R 0), if the conversion of the limiting reactant is very low, the volume of the reaction mixture could be taken as constant and R is not involved in the solutions of the models (since RxA can be taken as approximately zero).


89

In the general case, the concentration of a gas reactant A is N A,i (1 xA ) NA V Vi (1 R xA ) ( Pi P )(T Ti )  1 xA   P   Ti  CA,i     1 R xA   Pi   T 

CA

(3.93)

where NA are the moles of the reactant and V the total volume of gas phase. Then, for constant P and T (isobaric and isothermal operation, respectively), 1 xA NA CA,i 1 R xA Vi (1 R xA )

(3.94)

CB,i NB b  CA,i xA   Vi (1 R xA ) 1 R xA a  1 R xA 

(3.95)

CA and the concentration of B is CB

In the case of R 0 and constant P and T, CA CA,i (1 xA )

(3.96)

b CB CB,i CA,i x a

(3.97)

where CA,i is the initial (feed) concentration of A. Note that the volume change in a system involving a gas component could be a result of • • • •

a change in the total number of moles due to the reaction (R ≠ 0), a change in temperature (T ≠ Ti), a change in pressure (P ≠ Pi), or a combination of the above cases.

In reactions involving only liquid components without phase change, the pressure and temperature variation do not have any significant effect on the volume of the reaction mixture, and at the same time, the expansion factor is always zero. Thus, VVi in batch or Q Qi in continuous-flow systems and eqs. (3.96) and (3.97) are applicable. Example 1 If P and T are constant, use the following Levenspiel’s relationship for the fractional change in the volume of the system between no conversion and complete conversion of the reactant, where x is the conversion of reactant: R

V( x1) V( x0) V( x0)

90


to derive the definition of the expansion factor (eq.(3.88)). Solution Under constant P and T, R

V( x1) V( x0) V( x0)

N ( x1) N ( x0) N ( x0)

Since N ( x0) N tot,i N ( x1) N tot,i N A,i and this gives the definition equation (3.88) R

N A,i N tot,i

Example 2 Suppose a gas mixture consisting of 18% O2, 3% SO2 and 79% nitrogen is fed into a reactor so that the following reaction takes place: SO2 1 2 O2 SO3 Express the concentration of SO2 and O2 as a function of conversion. Solution The limiting reactant is SO2 and thus, by using the stoichiometry of the reaction, we have N tot,i 18 3 79 100 N SO2 3 (1 xSO2 ) N O2 181.5 xSO2 N SO3 3 xSO2 Then 1 0 1 0.5 0.5 1 3 R (0.5) 0.015 100


91

Then CSO2 CSO2,i CO2

1 xSO2 1 0.015 xSO2

CO2,i 1 0.015 xSO2

0.5

CSO2,i xSO2 1 0.015 xSO2

Example 3 Consider the gas-phase reaction 2A B 2C which is carried out isothermally and isobarically. The reaction rate is first order in A and first order in B. The feed concentration of A and B is 0.5 mol/L. Express the rate of reaction –rA solely as a function of conversion, evaluating all possible parameters involved. Solution First of all, we have to determine which the limiting reactant is. The way to determine which reactant is limiting is to divide the moles of each reactant by the coefficient from the balanced equation associated with that reactant. The smallest number that comes out indicates which reactant is the limiting one. This reactant limits how much of every other species made or needed for the reaction. For A, this calculation gives 0.25 and for B, 0.5. Thus, A is the limiting reactant and the calculation of , R, and x should based on it. The parameter is

c d a b 2 0 2 1 1 a 2 2

The expansion factor R is R

N A,i N tot,i

0.5  1    0.25 1  2

The moles of A and B after a conversion level of A equal to x are N A N A,i (1 x ) N B N B,i N A,i

x 2

The concentrations of A and B are CA CB

1 x A 1 x A NA C A,i 0.5 1 R x A 1 0.25 x A Vi (1 R x A )

C A,i x A C B,i 1 0.5 x A NB 0.5 1 0.25 x A Vi (1 R x A ) 1 R x A 2(1 R x A )

92


where xA the conversion of A xA

N A,i N A,o N A,i

Then  1 x A   1 0.5 x A  (rA ) kC A C B k 0.5  0.5   1 0.25 x A   1 0.25 x A  (1 x A )(1 0.5 x A ) 0.25k (1 0.25 x A ) 2 Expansion factor in gas–solid adsorption systems The meaning of the expansion factor is the same in gas adsorption. Consider the adsorption of a species (A) from the gas phase. A (g) inerts A (s) where A(s) denotes that the solute (A) is in the solid phase. Although this is not a reaction, it has the same result; the removal of the solute from the gas phase is equivalent to the consumption of a species in a reaction. For such a case, A(s) is not taken into account for the volume change in the gas, and thus R 1, and in the absence of inerts, R 1; whereas if the inerts are in great excess, R ≅ 0. This result is similar to the one in three-phase systems, as presented in Section 3.4.5. 3.3.4 Space-time and space velocity in flow reactors Space-time in flow reactors The time required to process one reactor volume of feed at specified conditions is called “space-time” and is defined normally at actual entering conditions (Levenspiel, 1972).

VR Qi

(3.98)

where: Qi the volumetric flow rate in the entrance of the reactor VR the reactor volume. Space-time is commonly referred to as “mean residence time,” “holding time,” or simply “residence time.” However, for a system with expansion (variable density system), these quantities are not equal and the residence time is a variable (Levenspiel, 1972): t m,z

VR VR Q z Q i (1 R x )

(3.99)


93

where: Qz the volumetric flow rate in the reactor at length z x the conversion of the limiting reactant at the same length R the expansion factor. In fixed-beds, the space-time as defined above is the superficial space time, as it is based on the total volume of the bed. The real space-time for a bed of bed voidage is

V R Qi

(3.100)

Another important feature related to mean residence time in fixed-beds is the fluid holdup based on the empty bed volume he,t. If the fluid occupies the whole empty bed volume, then he,t . However, this is not the case when he,t , i.e. when there is a bypass of the fluid from some regions in the bed, most commonly in the upper section of the bed in a downflow operation, and the fluid is a liquid. In this case, the real residence time is tm

h e,t VR Qi

(3.101)

The actual residence time of a reactor is measured by employing residence time distribution (RTD) experiments utilizing tracing techniques. Furthermore, several correlation forms estimating the fluid holdup can be found in the related literature. Space velocity in flow reactors The number of reactor volumes of feed at specified conditions, which can be treated in a unit time is called “space velocity” and is (Levenspiel, 1972) 1 Q s i VR

(3.102)

This parameter is frequently used in ion-exchange and adsorption operations in fixed-beds and it is frequently called “relative volumetric flow rate”: s

Q i Q relVR Q rel VR VR

(3.103)

The most common unit of Qrel is bed volumes per hour (BV/h). Space velocity is also used in catalytic reactors, especially in three-phase fixed-beds, and is referred to as liquid hourly space velocity (LHSV) for the liquid phase, and gas hourly space velocity (GHSV) for gas phase. As mentioned above, space-time and space velocity are measured under the entrance conditions. However, for space velocity, other conditions are frequently used (Fogler, 1999). For example, the LHSV is measured at 60 to 75 °F, and GHSV at standard temperature and pressure.

94


3.3.5 Hydraulics Hydrodynamic analysis of agitated vessels The impeller Reynolds number is defined as follows: N Re

Da2 N L

L

(3.104)

where: N Da L L

the impeller rotational speed, r/s the impeller diameter, m the liquid density, kg/m3 the dynamic liquid viscosity, Pa s.

The flow is called turbulent in the case NRe 10,000, whereas the flow is laminar in the case NRe 10. In the case 10 NRe 1000, the flow is characterized as transient (Perry and Green, 1999). The following equations relate velocity head, pumping rate, and power under turbulentflow conditions: Q N Q ND a3

(3.105)

N P N 2 D a2 N Qg

(3.106)

H

 D5  g P N P L N 3  a  L HQ gc  gc 

(3.107)

where: Q NQ NP H P gc g

the impeller discharge rate, m3/s the discharge coefficient, dimensionless power number, dimensionless, the velocity head, m the power, Nm/s = J/s = W 1 when using SI units the gravitational acceleration, m/s2.

It should be noted that for relatively dilute solid–liquid mixtures, except for fibrous solids, the power to agitate at a given speed is essentially the same as for the clear liquid (Treybal, 1980). Concentrated slurries and suspensions of fibrous solids are likely to be nonNewtonian in character. Given the delivered power P and the friction losses, the required motor power Pm can be calculated as Pm

P 100 % losses

(3.108)


95

These losses are from the loss of usable energy in the form of work through the motor, shaft gearing, and bearings, and could be as high as 20–30%. The agitation intensity could be characterized as mild, vigorous, and intensive, depending on the power delivered per unit volume of liquid. For water, the approximate values are presented in Table 3.2. The discharge rate has been measured for several types of impellers, and discharge coefficients have been calculated (Perry and Green, 1999). For turbines, NQ ranges from 0.7 to 2.9, depending on the impeller geometrical characteristics. For a standard flat blade turbine (Ruhston) in a baffled vessel, this value is 1.3. For a four-blade 45° turbine and a baffled vessel, this value is 0.87, and for marine propellers and a baffled vessel, it is 0.5 (McCabe et al., 1993). More specifically, for the typical case of six-bladed turbines (Ruhston), the following approximation can be used (Perry and Green, 1999): N Q 0.93

DT Da

(3.109)

where DT is the tank diameter in m. Power number Np ranges from 0.35 to 7, depending on the impeller type, its geometrical characteristics, and vessel design. This number is very important since it is needed for the evaluation of power consumption, which in turn, is used for the determination of mass transfer coefficients. The geometry of the agitation system plays a critical role. The typical geometrical ratios are presented in Table 3.3. In Figure 3.23, the various dimensions are presented. Baffles are frequently arranged with a clearance, which may be half the baffle width, between the baffle and the tank wall to prevent accumulation of solids behind the baffles (Treybal, 1980). The height of the impeller above vessel floor is frequently called “clearance,” and is the distance between the vessel bottom and the impeller centerline. On the other hand, Cb is the distance from the bottom to the bottom of the impeller. The relationship between these two parameters is Za Cb

W 2

(3.110)

In Table 3.4, the power number is presented for several cases. The values of NP are approximate. At low Reynolds numbers, about 300, the power number curves for baffled and unbaffled tanks are identical (McCabe et al., 1993). For higher NRe, the power number for unbaffled tanks is lower than the values for baffled tanks.

Table 3.2 Agitation intensity versus power/volume for water Agitation

Power/volume (kW/m3)

Mild Vigorous Intensive

0.09–0.17 0.34–0.51 0.68–1.69

96


Table 3.3 Geometrical ratios (Perry and Green, 1999; Treybal, 1980; McCabe et al., 1993; Nouri and R.M.Hockey, 1998; Armenante and Nagamine, 1998; Pavlov et al., 1979; Fishwick et al., 2003; Kato et al., 2001; Rewatkar et al., 1991) Ratio

Description

Range

DT/Da Za/DT Da/W DT/B

Tank diameter/impeller diameter Height of impeller above vessel floor /tank diameter Impeller diameter/width of blade Tank diameter/affle width

HL/DT –

Liquid depth in vessel/tank diameter Number of impeller blades

2.64–3.7 (typically 3) 1/3 3–8 6–25 (typically 10–12) 0.67–1.5 (typically 1) 3 (Propellers) 6 (Turbines) 2 (Paddles) 0–45—60° (angle) 1–2 Da (pitch) 4

length/degrees –

Pitch/angle Number of baffles

B

Da

HL

w Za

Cb DT

Figure 3.23 Dimensions of agitated vessels.

Typical values for the rest of the geometrical ratios and other characteristics are given in Table 3.3. Generally, the higher values of NP in Table 3.4 correspond to the higher Reynolds numbers, where NP becomes practically constant (Figure 3.24). For Rushton turbines (flat-blade disc turbines), the following equation can be used (Nouri and Hockey, 1998): N P 1.98 Re 0.082

(3.111)


97

Table 3.4 Power number for several types of impellers and vessel characteristics (Perry and Green, 1984, 1999; McCabe et al. 1993; Nouri and Hockey, 1998) Impeller type

Da/W

DT/B

No. of blades

Pitch/angle

NP

NRe (approximate values)

Propellera Propellera Pitched-blade turbine Pitched-blade turbine Pitched-blade turbine (6)b Straight-blade open Turbine (4) Straight-blade open turbine (2) Curved-blade open turbine (5) Curved-blade open turbine Flat-blade disc turbine (3) Flat-blade disc turbine Flat-blade disc turbine (1) Flat-blade disc turbine Flat-blade disc turbine Flat paddle Flat paddle Flat paddle Flat paddle Flat paddle

– –

10 10

3 3

Pitch 2Da Pitch Da

0.9–1 0.32–0.35

103 104

4

10

6

60°

2.2–2.5

3 102

8

12

6

45°

1.5

104

8

12

6

45°

1.3-1.5

103

8

12

6

–

2.7-3

104

5

10–12

6

–

4

2 103

8

10–12

6

–

2.7–2.8

104

5

10–12

6

–

4.8–5

104

8

10–12

6

–

3

104

5

6

6

–

7

104

5

10–12

6

–

5–5.75

104

–

10

4

–

5.31

104

5 4 6 8 6 6

25 10 10 10 10 10

6 2 2 2 4 6

– – – – – –

4 1.8-2.25 1.7 1.15 2.75 3.82

104 104 104 104 104 104

a b

The same value holds for the angular off-center position of the impeller in an unbaffled vessel. The number in parenthesis corresponds to the curves in Figure 3.24.

For NRe 8 104 and NP ≅ 5, Nouri and Hockey used a typical Rushton impeller and the following vessel characteristics: DT HL, 4 baffles with B DT/10, Da DT/3, Da/W 5.4, and Za/DT 1/3. The minimum volume of the vessel is equal to the volume of the liquid to be treated, and thus for a cylindrical vessel we have VL

D T2 HT 4

(3.112)

98


100

NP

50

10 1

5

4

2

3 5

2

6

1 1

10

100

1000

10000

NRe

Figure 3.24 Dimensionless power number in stirred tanks for a typical configuration (Perry and Green, 1999). A typical configuration consists of a baffled tank with DT/B values of 10 – 12 and a six-blade impeller.

Since in design practice HL = DT, we have 4  D T  VL   

1 3

(3.113)

Using this relationship and the ratios as presented in Table 3.3, the appropriate impeller diameter Da, Reynolds number, and the other geometrical characteristics of the agitated tank can be calculated. Minimum rotational speed for complete solid suspension in solid-liquid systems The following equation of Zwietering is used for liquid–solid systems and agitated vessels (McCabe et al. 1993; Zwietering, 1958; Dohi et al., 2002):

0.13 0.1 N js SX %

d p0.2  g  D 0.85   a

0.45

L

where: Njs X% v

the minimum (critical) rotational speed, r/s the solid loading, wt.% the kinematic viscosity of the liquid, m2/s the density difference between solid and liquid, kg/m3 .

(3.114)


99

For the rest of the parameters, SI units are used. For the evaluation of density difference, the hydraulic density for the solid phase is used: h L (1 p )( s L )

(3.115)

where: p the particle porosity s the skeletal density of the solid. The parameter S is called the proportionality constant or shape factor, and depends on the impeller and vessel geometry (Armenante and Nagamine, 1998): D  S s1  T  D  a

s2

 C  exp  s 3 b   DT 

(3.116)

where Cb is the impeller clearance measured from the bottom of the impeller to the bottom of the tank, in m. The constants si depend upon the type of the impeller, and for cylindrical baffled vessels, are presented in Table 3.5 (Armenante and Nagamine, 1998). These values were obtained for 1/48 Cb/DT 1/2.5. The values of S given by McCabe directly for several types of impellers (McCabe et al., 1993) are shown in Table 3.6.

Table 3.5 Constants of parameter S. Impeller type

s1

s2

s3

Flat blade disc turbine with six blades Straight blade open turbine with six blades Pitched-blade (45°) turbine with six blades Fluidfoil Chemineer HE-3 impeller

0.99 1.43 2.28 3.49

1.4 1.2 0.83 0.79

2.18 1.95 0.65 0.66

Table 3.6 Value of the shape factor S. Impeller type

DT/Da

DT/Za

S

Flat-blade disc turbine with six blades (Da/W 5, Np 6.2)

2 3 2 3 4 3 4 4

4 4 4 4 4 4 4 2.5

4.1 7.5 4.8 8 12.5 6.5 8.5 9.5

Paddle with two blades (Da/W 4, Np 2.5) Propeller with three blades (Np 0.5)

100


3.3.6 External mass transfer in liquid–solid systems The slip-velocity theories are based on the correlations of steady state transfer to particles fixed in space, with the average slip velocity used to calculate the Reynolds number. When natural convection effects are absent and when the Reynolds number is greater than 1, the transfer rate for single spheres is given by the semitheoretical equation (Harriot, 1962) Sh 2 0.6 Re p0.5 Sc 0.33

(3.117)

Here, the particle Reynolds number is based on the slip velocity. If terminal velocity is used, then the above correlation gives the minimum value for the mass transfer coefficient. Minimum mass transfer coefficients further depend on the density difference between solid particles and solvent. For the typical case of water, the approximate values presented in Table 3.7 can be used (Harriot, 1962). These values hold for particle diameters of 100 – 10,000 m or 0.1 –10 mm, covering all practical applications. Furthermore, the typical density difference is about 1, since particle densities are around 2 g/cm3. Harriot (1962) measured the mass transfer coefficients in baffled tanks, using six-blade turbines and several liquids such as water and glycerine. According to that study, • •

• • •

Mass transfer coefficients are probably the same for any stirrer location if the particles are completely suspended. For small ion-exchange particles in water, the mass transfer coefficient decreases with increasing particle size, but is almost independent of size for particles larger than about 200 m. Viscosity has only a small effect on the mass transfer coefficient. The effect of particle shape was not determined, but is expected to be of minor importance. The coefficients in unbaffled tanks increased with only the 0.3 power of the stirrer speed. At the speed needed for complete suspension in a baffled tank, the coefficients are about the same with or without baffles. At higher speeds, the more uniform dispersion of the particles and the greater velocity fluctuations make the coefficients larger with baffles present.

Table 3.7 Mass transfer coefficient range (approximate values). Density difference (g/cm3)

Minimum mass transfer coefficient range (cm/s)

3 1 0.3 0.1

0.008–0.01 0.005–0.007 0.003–0.005 0.0025–0.003

3.4 Slurry Reactors

101

Then, the actual mass transfer coefficients, which cover a hundred-fold range, are about 1.5 –8 times that predicted from the correlations for fixed particles if the terminal velocity is used to calculate the particle Reynolds number. McCabe gives the narrower range of 1.5 –5, for a wide range of particle sizes and agitation conditions (McCabe et. al., 1993). Using these values and Table 3.7, we can calculate the ranges of the actual mass transfer coefficients. The solid–liquid mass transfer coefficient without aeration is a function of power consumption per unit volume of the liquid. One typical case is the Levins and Glastonbury correlation for small particles ( 5, kf dpe where

Dfg

 usL L dpe  0.75   

0.5

L

 L     L Dfg 

 usG G dpe  



0.55

 usL L dpe  



0.7

0.33

(1 4)

G

(3.438)

(3.439)

L

In these equations, CGS units should be used. Liquid–solid interfacial area in trickle and packed bubble bed reactors By definition, the total external particle area per reactor volume is (Smith, 1981) au

Aparticles,total

(3.440)

VBED,total

The external area and the volume of a single spherical particle are Aparticle dp2

(3.441)

dp3

(3.442)

Vparticle

6

The mass of the single particle is M particle Vparticle p

(3.443)

and the total mass of particles in the bed is M particles,total Vparticle p nparticles

(3.444)

From the definition of the bulk density, b

M particles,total VBED,total

$VBED,total

M particles,total b

(3.445)

then VBED,total

Vparticle p npartilces b

Vparticle nparticles 1

dp3 nparticles 6(1 )

(3.446)

3.8 Two-Phase, Fluid–Solid Fluidized Bed Reactors

189

and finally,

au

Aparticles,total VBED,total

dp2 nparticles   d n    6(1 )  3 p particles

6(1 ) dp

(3.447)

3.8 TWO-PHASE, FLUID–SOLID FLUIDIZED BED REACTORS 3.8.1 General The requirement for mechanical agitation can be avoided by using a fluidized bed reactor. In this type of reactor, the agitation and mixing are achieved by means of the moving liquid that carries the solids through the reactor or mixes with the particle phase. Thus, high heat and mass transfer rates are assured. Fluidized beds are used in both catalytic and noncatalytic systems. Typical examples of catalytic uses are hydrocarbon cracking and reforming, oxidation of naphthalene to phthalic anhydride, and ammoxidation of propylene to acrylonitrile. Examples of noncatalytic uses are roasting of sulfide ores, coking of petroleum residues, calcination of ores, incineration of sewage sludge, and drying (Perry and Green, 1999). Fluidized beds are also extensively used in wastewater treatment (aerobic and anaerobic treatment of municipal and industrial wastewaters). In this application, they are specifically called “biological fluidized beds.” Sand or granular activated carbon is usually used as a carrier for the growth of biological organisms. Fluidized beds are also found in adsorption and ion-exchange processes, where they are used if the influent contains significant amounts of suspended matter, or the solid is of very low size, like PAC. The upward superficial velocity of the gas is usually between 0.15 and 6 m/s. Generally, bed heights are not less than 0.3 m or more than 15 m (Perry and Green, 1999). The employment of fluidized beds has many disadvantages. First of all, uniformity of temperature is achieved, even for highly exothermic reactions, since heat transfer is conducted by convection by means of the continuous motion of the particles. As a result, the heat-transfer coefficient from the fluidized bed to the heat-exchanger surface is tens of times that for a fixed bed. Intensive heat removal from the bed does not result in extinction of the reaction in the catalyst bed, and processing of gas mixtures with high concentrations of reactants does not lead to overheating of the catalyst. The isothermal conditions within each layer of the fluidized bed allow the automatic stabilization of the temperature regime inside the reactor. In the case of exothermic reactions, higher yields of products can be achieved in fluidized beds in comparison to fixed beds, due to temperature uniformity (Mukhlyonov et al., 1979; McCabe et al, 1983). Another feature in favor of fluidized beds is the increased productivity of the catalystdue to the smaller size of the particles, which in turn leads to better utilization of the surface area of the catalyst. In addition, the removal of solids from a fluidized bed is an easy task (Mukhlyonov et al., 1979).

190


However, there are also some drawbacks associated with the use of fluidized beds. The complete mixing of the gas phase in this type of reactor decreases the process driving force. Moreover, the formation of large bubbles makes the process less efficient and difficult to handle. However, the main disadvantages of fluidized beds are the erosion of the reactor, the attrition of the solids, and the irregular conduction between the gas and the solid phase (McCabe et al., 1983). 3.8.2 Hydraulics of fluidized beds Fluidization regimes The operation of fluidized-bed reactors can be seen as the transition region between continuous-stirred tank and packed-bed reactors. In a fluidized bed, a bed of solid particles is fluidized by the upward flow of the gas or liquid stream, which may be inert or contain material relevant to the reaction. The several fluidization regimes are shown in Figure 3.51. Because of greater mechanical damage catalyst particles at high fluid velocities, and because of the poor retentivity of fines produced by attrition in beds of large particles, the use of small-diameter catalyst particles in fluidized beds is more usual (Gunn, 1968). In many applications of fluidization, the particles are in the range 30 –300 µm (Smith, 1981;

Figure 3.51 Fluidization regimes.


191

McCabe et al., 1983). However, very small particles frequently act as if damp, even though dry, forming agglomerates or fissures in the bed, or spouting (Perry and Green, 1999). According to Gunn (1968), the grinding of solids to very small particle sizes is expensive, and as a result in many cases particles of sizes greater than 0.5 mm are used, as in the case of combustion of coal, or even as large as 6 cm in other applications (Perry and Green, 1999). However, large particles cause instability and result in slugging or massive surges. For velocities higher than the minimum fluidization velocity, the appearance of fluidized beds is often quite different (McCabe, 1983). In most liquid systems, as the velocity is increased, the motion of the particles becomes more vigorous, whereas the bed density at a given velocity is the same in all sections of the bed. This is called “particulate fluidization” (or smooth fluidization/nonbubbling fluidization/homogeneous fluidization, Figure 3.51) and its characteristic is the large but uniform expansion of the bed at high velocities. This type of fluidization appears when the fluid and the solids have similar densities. In contrast, the density difference is very high when the fluid is a gas, which results in the so-called bubbling fluidization (or aggregative fluidization or heterogeneous fluidization)—the gas moves through the reactor either forming “bubbles” that contain relatively few solid particles, or as a continuous “dense” phase where the particle concentration is high (particulate or emulsion phase). Although, in general, liquids are associated with particulate fluidization and gases with bubbling fluidization, it is not always the case. The density difference is the decisive parameter and thus bubbling fluidization appears in water systems of heavy solids, and particulate fluidization in high-pressure gas systems of fine particles (McCabe, 1983). However, a gas is usually the fluid in fluidized beds and the bubbling regime prevails (Smith, 1981). Industrial reactors, particularly for solid-catalyzed gas-phase reactions, often operate in that regime, with typical values of gas velocities in the range 5–30ufm or even 250ufm, where ufm is the minimum fluidization velocity (Levenspiel, 1972). Another type of fluidization is the slugging fluidization. It represents the case where the bubbles form slugs of gas, usually when the size of the bubbles is about one-third the diameter of the bed. In general, slugging is undesirable because it is accompanied by high pressure, which may cause dangerous vibrations to the reactor. Finally, it should be noted that in the case of multisized solids, the operating velocity should be higher than the minimum fluidization velocity of the largest particle and smaller that the elutriation velocity of the smallest particles. Geldart (1973) classified powders into four groups according to their fluidization properties by air at ambient conditions. This classification is now used widely in all fields of powder technology. •

•

Group A powders: They give a region of nonbubbling fluidization beginning at the minimum fluidization velocity (ufm), followed by bubbling fluidization as fluidizing velocity increases. This velocity limit is called minimum bubbling velocity (ubm > ufm). These materials have small mean particle size (dp < 30 µm) and/or low particle density ( ufm and Rep < 10, the relationship between the fluidized bed voidage and velocity can be derived from the Ergun equation (McCabe et al., 1983): 150 us s 2 dp 2 g ( h )

3f 1 f

(3.471)

The expanded bed height is Zf Z

1 1 f

(3.472)

where Z is the fixed bed height and is the fixed bed voidage. For relatively large particles (of several millimeters) in water, the equation proposed by Lewis, Gilliland, and Bauer (LGB) can be used (McCabe et al., 1983):  u  f  s   ufm 

1 m

(3.473)

The exponent m is a function of particle Reynolds number based on the minimum fluidization velocity. It can be estimated by the following correlation: 0.0804 m 4.21Re fm

(3.474)

200


for 1 < Refm < 1000. In this region, m is approximately between 2.5 and 4.2. The value of m is between 4.2 and 4.5 for 0.1 < Refm us > ufm:  18 Rep 0.36 Rep2  f   Ar  

0.21

(3.475)

It is easy to show that for the same superficial velocity, smaller particles result in higher bed porosity. Bed voidage in type B fluidization The following analysis holds for Type B fluidization and for Type A bubbling fluidization, when the region of particulate fluidization is so small that it can be ignored. In the framework of the two-phase model (see the subsection Hydrodynamic modeling of bubbling fluidization), the bed expansion in terms of the fraction of the bed occupied by bubbles bub is bub

Z f Z fm us ufm Zf ubub

(3.476)

where: Zfm the bed height at incipient fluidization Zf the fluidized bed height ubub the mean rise velocity of a bubble in the bed. According to Grace (1984), in practice, the bubble volume fraction bub never exceeds 0.4. If a higher value is calculated, bub should be taken as 0.4, or the bed may be operating in the turbulent fluidization regime, invalidating the basis of the two-phase and bubbling bed models (see the subsection Hydrodynamic modeling of bubbling fluidization). Then, Zf

Z fm 1 bub

(3.477)

The mean bed voidage is (1 f ) (1 bub )(1 fm )

(3.478)

where fm is the bed voidage at the minimum fluidization value of velocity. Then, in order to evaluate the fluidized bed height and the corresponding voidage, the ubub value is needed (see the subsection Hydrodynamic modeling of bubbling fluidization).


201

The use of internals can improve the smoothness of fluidization, when this is desirable. The use of vertical cylindrical tubes immersed in the bed is a representative example. These tubes are sufficiently long to extend from the distributor to the top of the bed. For this type of beds and for perforated plate distributors, the following correlation can be used for the estimation of fluidized-bed porosity in gas–solid fluidization (Hilal, 2000):  fm  us ufm  f uter ufm  1 fm 

w

(3.479)

where: w 1.28 0.00033P 3

(3.480)

where uter is the terminal velocity of the particles and P is the hole pitch of the perforated plate. A square pitch of 7–12 mm is typical. Criteria for distinguishing fluidization regimes The minimum bubbling velocity for Group A particles (or more generally, for Type A fluidization) and gas–solid systems is (Abrahamsen and Geldart, 1980; Ye et al., 2005) ubm

2.070.06 G dp exp(0.716 X 45 )

0.347 G

(3.481)

where X45 is the fraction of particles with size smaller than 45 µm and dp the sieve diameter. In this equation, SI units should be used. In the region of Group B particles (for ambient conditions and air as gas phase) or in the Type B fluidization regime (for other conditions), where ubm = ufm, the above equation results in ubm < ufm, which is not acceptable. For example, for ambient conditions and air as gas, if dp = 0.24 mm and h = 1500 kg/m3, we are in the region of Group B particles (Figure 3.52) and the result is ubm = 0.022 m/s, while ufm = 0.028 m/s. Using the Bayens equation (3.460) and eq. (3.481), the ratio of minimum bubbling velocity to minimum fluidization velocity is ubm 23000.126

0.523 exp(0.716 X 45 ) G G 0.8 ufm dp ( p G )0.934 g 0.934

(3.482)

ubm um b For type B fluidization, we obtain ( ufm = 1), while for type A fluidization ( ufm 1). In the second case, the fluidization regime is • •

particulate, if ufm us ubm type A bubbling, if us ubm

In Figure 3.54, the ratio of the minimum bubbling velocity to the minimum fluidization velocity is plotted for air at 20 °C, and p is 1000–3000 kg/m3. From Figure 3.52 and the

202


specific density difference, the particle size is kept within the appropriate region, within 0.035 dp 0.1 mm, depending on the density difference, to be in the Group A particles regime (Type A fluidization). Since the minimum bubbling velocity could not be less than the minimum fluidization velocity, the ratio is set equal to unity when ubm ufm. In Figure 3.54, it is clear that while approaching the limit of Group B particles (large particle density and particle size), ubm approaches ufm, as expected. On the other hand, the difference between the two velocities is increased constantly for smaller particles and lower particle density, while it is very high for dp 0.045 mm where X0.45 = 1. This could be useful in the case where an expanded region of particulate fluidization is desirable in gas–solid systems. Since in the region between ufm and ubm the fluidization is particulate, eq. (3.475) could be used for the determination of bed voidage at minimum bubbling velocity, setting us ubm. In Figure 3.55, bm and fm are compared to each other for Group A particles—an air system at 20 °C (S 1, 0.4, p 1000–3000 kg/m3, X45 0 for dp 0.045 mm, and X45 1 for dp 0.045 mm). From Figure (3.52) and the specific density difference, the particle size is kept within the appropriate region, within 0.035 dp 0.23 mm, depending on the density difference, to be in the Group A particles regime. Pavlov’s equation (3.475) is used for voidage determination at minimum bubbling conditions (for us ubm), and since dp 0.5 mm, the Broadhurst–Becker equation (3.466) is used for fm. For a wide range of particle sizes, the values of fm and bm are close to each other, within a ratio between 0.95 and 1.1 for all particle densities. Values of bm/fm ratio 9 3000 kg/m3 2000 kg/m3

8

1000 kg/m3 7

ubm/ufm

6 5 4 3 2 1 0 0

0.02

0.04

0.06

0.08

0.1

0.12

dp (mm)

Figure 3.54 Ratio of minimum bubbling velocity to minimum fluidization velocity. For dp > 0.045 mm, X45 = 0 and for dp < 0.045 mm, X45=1 is assumed.


203

1.3 3 g/cm3 2 g/cm3 1,5 g/cm3 1 g/cm3

εbm/εfm

1.2

1.1

1

0.9 0

0.05

0.1

0.15

0.2

0.25

dp (mm)

Figure 3.55 The values of bm and fm versus dp.

lower than 1 are not acceptable since bm cannot be lower than fm. In these cases, the ratio is set equal to unity. Foscolo and Gibilaro (1984) proposed the following criterion for both gas and liquid–solid systems: ( gdp )0.5  h f    u ter

0.5

0.56 n(1 t )0.5 nt 1

(3.483)

h

where t is the voidage at which a transition from particulate to bubbling fluidization occurs, and n is the exponent in the Richardson–Zaki equation (2.4 n 4.8). In practice, t is taken equal to bm. The terminal velocity uter can be estimated using Table 3.12. The Foscolo–Gibilaro equation indicates multiple solutions for t. However, two solutions at maximum are to be expected in the operating range between 0.4 and 1. The second solution exists because bubbling beds are known to revert to particulate behavior at high voidages (Foscolo and Gibilaro, 1984). To demonstrate the behavior of the Foscolo–Gilibaro equation, the following form is used: FC [0.56 n(1 t )0.5 nt 1 ] A 0

(3.484)

where A

( gdp )0.5  h f    u ter

h

0.5

(3.485)

204


Table 3.12 Equations to calculate n and uter for different Reter Reter

n

Reter 0.2

4.65 20(dp/D) 4.65

uter

dp2 ( p f )g 18

0.2 Reter 1.0

[4.35 1.75 (dp/D)]Re0.03 4.35⋅Re0.03

dp2 ( p f )g 18

1.0 Reter 2.0

2.0 Reter 200

200 Reter 500

Reter500

[4.45 18(dp/D)]Re0.1 4.45Re0.1

[4.45 18(dp/D)]Re0.1 4.45Re0.1

4.4Re–0.1

2.39

dp2 ( p f )g 18

0.153

0.153

dp1.14 [( p f )g ]0.71

0.43 0.29 f dp1.14 [( p f )g ]0.71

0.43 0.29 f

 d p ( p f ) g  1.74   f  

0.5

In Figure 3.56, eq. (3.484) is plotted for several values of A found in the original paper of Foscolo and Gilibaro (1984) (0.1–0.4) and for n = 3 representing an intermediate value of Reter (0.2 t2 > t1 > fm, the fluidization is of Type A and more specifically,

• particulate, in the regions fm 106.

To distinguish between particulate and bubbling fluidization, the Romero and Johnson criterion is useful (Kwauk and Liu, 2000): particulate fluidization exists if Frfm Refm

h f Z fm 100 f D

(3.487)

Frfm Refm

h f Z fm 100 f D

(3.488)

bubbling fluidization exists if

where Frfm is the Froude number at incipient fluidization: Frfm

2 ufm dp g

(3.489)

where: dp the particle diameter D the bed diameter Zfm the fixed bed height at incipient fluidization. Another simpler criterion is that of Wilhelm and Kwauk (Kwauk and Liu, 2000), which is based solely on the Frfm number–if it is lower than 0.13, particulate fluidization exists and if it is higher than 1.3, bubbling fluidization exists. Finally, in gas–solid fluidization, slugging will not occur provided the following criterion is satisfied (Beyens and Geldart, 1974): Z fm 1.9 D ( p dp )0.3

(3.490)


207

where dp is the mean sieve size of the particles. If the bed is deeper than this critical height, slugging will take place for a gas velocity higher than the minimum velocity of slugging: usm ufm 0.16(1.34 D 0.175 Z fm )2 0.07( gD)0.5

(3.491)

where Zfm is the height of the bed at incipient fluidization. In these equations, SI units should be used. Finally, in beds exhibiting low ratios of diameter to height, the bubbles can grow and become as large as the cross-section. As a result, the bubbles move through the bed separated by slugs of solids. This phenomenon is called “slugging” and has to be generally avoided because it can lead to pressure fluctuations and entrainment of solids as well (McCabe et al., 1983). Hydrodynamic modeling of bubbling fluidization (type B fluidization) The modeling of fluidized beds begins with the analysis of the two most important hydrodynamic flow models presented by Davidson (Davidson and Harrison, 1963) and Kunii and Levenspiel (1968). Two-Phase theory of Davidson According to the two-phase theory, two phases exist in the bubbling fluidized bed: (a) the bubbling phase consisting of gas bubbles, and (b) the particulate phase, namely the solids around the bubbles. The particulate phase is alternatively called “the emulsion phase.” Bubbles stay in the bubble phase and penetrate only a small distance into the emulsion phase. This zone of penetration is called “cloud” since it envelops the rising bubble. If the gas flow rate is higher than that required 1for the onset of fluidization, the gas will move through the bed in the form of bubbles. So, in bubbling fluidization, for a gas velocity above the minimum bubbling velocity, the formation of bubbles will lead to a bed expansion. In such a case, the condition of the particulate phase is unchanged and thus, the porosity of the particulate phase is constant after this point. At gas flow rates above the point of minimum fluidization, the fluidized bed resembles with a boiling liquid—bubbles of gas rise rapidly and burst on the surface, and the emulsion phase is thoroughly agitated. Davidson and Harrison (1963) were the first to state that the fluidized bed had to be treated as a two-phase system consisting of an emulsion phase and a bubble phase (often called the dense and lean phases, respectively). This was the basis of the so-called two-phase model (Figure 3.57). Focusing on the bubbles, it should be mentioned that they are not exactly spherical. They contain very small amounts of solids and have an approximately hemispherical top and a pushed-in bottom. Each bubble of gas has a wake that contains a significant amount of solids. These characteristics are illustrated in Figure 3.58. Consequently, during their journey in the reactor, the bubbles carry an amount of solids. The net flow of the solids in the emulsion phase must therefore be downward. The gas within a particular bubble remains largely within that bubble and only a small part of it penetrates a short distance into the surrounding emulsion phase, forming the so-called cloud.

208


Figure 3.57 General representation of the two-phase model.

Figure 3.58 Bubble and particulate phase.

Bubbling bed model of Kunii and Levenspiel Rowe and Partidge (1962) also found out that each bubble of gas drags a substantial wake of solids up the bed. On the basis of these findings, Kunii and Levenspiel (1972) developed the bubbling bed model. The assumptions used in that model are the following: • • •

The emulsion phase stays at minimum fluidizing conditions. Thus, the relative velocity of the gas and solid remains unchanged. All bubbles have the same size. The solids in the emulsion phase flow smoothly downward, essentially in plug flow.


•

209

The concentration of solids in the wake is the same as the concentration of solids in the emulsion phase, and therefore, the gaseous void fraction in the wake is also the same as in the emulsion phase. Because the emulsion phase is at the minimum fluidizing condition, the void fraction in the wake is equal to fm. The wake, however, is quite turbulent, and the average velocities of both the solid and gas in the wake are assumed to be the same and equal to the upward velocity of the bubbles.

Applying the appropriate material balances for the solids and the gas, the fraction of the bed occupied by the bubbles and wakes can be estimated using the Kunii–Levenspiel model. The fraction of the bed occupied by that part of the bubbles which does not include the wake, is represented by the parameter , whereas the volume of the wake per volume of the bubble is represented by . Consequently, the bed fraction in the wakes is and the bed fraction in the emulsion phase (which includes the clouds) is 1 – – . Then (Fogler, 1999) uss

ubub 1

(3.492)

The velocity of the gas in the emulsion phase is ue

ufm uss fm

(3.493)

The fm is present in this equation because ufm is the superficial velocity, i.e., based on the cross section of the empty tube. The velocity of the moving solids, uss, is positive in the downward direction here as in most of the fluidization literature. The velocity of the gas in the emulsion ue is taken as positive in the upward direction, but note that it can be negative under some conditions. The fraction of the bed occupied by bubbles is given by (Fogler, 1999)

us ufm ubub ufm (1 )

(3.494)

Kunii and Levenspiel assumed that the last equation could be simplified to (Fogler, 1999)

us ufm ubub

(3.495)

which is valid if ubub >> ufm. The total mass of solids in the bed (solids holdup) is Ms AZ f (1 )(1 fm ) p

(3.496)

where is the volume of wake per volume of bubble. According to Levenspiel (1972),

210


experiments showed that ranges between 0.25 and 1. Then, the fraction of the bed occupied by clouds is c

3ufm fm ubs ufm

(3.497)

Furthermore, Levenspiel defined the following parameters (Levenspiel, 1972; Fogler, 1999):   3ufm   c (1 fm )   (1 fm )  c      fm ubs ufm e

(1 fm )(1 ) ( c b )

(3.498)

(3.499)

where: b the volume of solids dispersed in bubbles per volume of bubbles c the volume of solids within clouds and wakes per volume of bubbles e the volume of solids in emulsion per volume of bubbles. Typical values of b, c, and e are 0.005, 0.2, and 1.5, respectively (Levenspiel, 1972; Fogler, 1999). A weakness of this model is that there does not yet exist a reliable method or correlation for determining the value of b. In Figure 3.59, the flow pattern in a fluidized bed is shown. Bubble velocity and diameter In the context of the bubbling bed theory, the rise velocity of a single bubble in a fluidized bed is given by (Wen, 1984) ubs 0.711( gdbub )0.5

(3.500)

whereas the rise velocity of bubble, cloud, and wake is ubub us ufm 0.711( gdbub )0.5

(3.501)

The values of the rise velocity of the bubbles estimated by the equation above are generally higher than the ones predicted by the two-phase-model of Davidson. This difference can be explained as follows. The single-bubble velocity can be affected by various other factors, especially when many bubbles are present. Specifically, an increase in the number of bubbles leads to a lower drag force on each bubble. In other words, the bubbles carry each other up through the bed. The number of bubbles formed depends on the gas velocity. The greater the gas velocity, the greater the flow rate and thus, the more the number of bubbles formed. To sum up, an increase in the gas velocity results in an increase in the real bubble velocity. The bubble velocity is also affected by the viscosity of the gas and the size and density of the solids indirectly, since all these parameters affect the minimum fluidized velocity. Specifically, a low viscosity and a large size of particles with high density increases the minimum fluidization velocity, which in turn leads to a decrease in the velocity of the rising bubble.


211

Figure 3.59 Flow pattern in a fluidized bed based on the three-phase model of Levenspiel–Kunii.

The following equation correlates the initial bubble size with the type of distributor plate. For porous plates (Mori and Wen, 1975), dbub,o 0.00376(us ufm )2

(3.502)

whereas for perforated plates,  D 2 (us ufm )  dbub,o 0.347   4n  

0.4

(3.503)

where n is the number of perforations (orifices on the plate). For typical distributors, n is in the range 4–3100. In these equations, CGS units should be used. The maximum bubble size can be evaluated as follows:   dbub,max 0.652  D 2 (us ufm )  4  

0.4

(3.504)

212


In this equation, CGS units should be used. So, the actual bubble diameter at height z above the bed bottom is dbub,max dbub dbub,max dbub,o

 0.3z  exp   D 

(3.505)

This equation has been deduced from studies conducted with bed diameters of 7.6–130 cm, minimum fluidization velocities of 0.5–20 cm/s, solid particle sizes of 0.006–0.045 cm, and us – ufm 48 cm/s. To calculate an average value of the bubble velocity, an average bubble diameter should be used. This diameter can be taken to be equal to the bubble diameter at z Hf /2. Thus, to calculate the bubble diameter and thus the bubble velocity, the fluidized bed height should be known. To solve the problem, an iteration method should be used (Figure 3.60). In the original eq. (3.505), the height above the distributor plate h is used instead of z. However, practically, h and z are equal. The correlation of Werther, based on a statistical coalescence model, is an alternative to estimate dbub (Wen, 1984). dbub 0.853[1 0.272(us ufm )]1 3 (1 0.0684 z)1.21

(3.506)

In this equation, CGS units should be used. Darton et al. (1977) and Werther (1983) presented different relationships for bubble diameter and bubble velocity for Group A and Group B particles (for bubbling fluidization). The mean rise velocity of a bubble in the bed (ubub) can also be evaluated using the following equations, which include a wall effect correction (Darton et al., 1977; Werher, 1983; Wen, 1984).

Guess Zfm

Calculate dbub at Zfm / 2

Calculate ubub

Calculate Zfm

Figure 3.60 Iteration procedure.


213

For Group B powders (Type B fluidization) dbub and

0.54 (us ufm )0.4 ( z 4 N 0.5 )0.8 g 0.2

ubub bub ( gdbub )0.5

(3.507)

(3.508)

where: dbub z N bub

the equivalent volume diameter of the bubble the height above the fluid distributor (bed bottom) the number of holes per unit area in the distributor, 1/m2 a constant dependent on the bed diameter D: 0.64  bub 1.6 D 0.4 1.6 

for D 0.1 m for 0.1 D 1 m for D 1 m

To calculate an average value of the bubble velocity, an average bubble diameter should also be used, which can be taken to be equal to the bubble diameter at Z Hf /2. An iteration method should be used (Figure 3.60). As an initial Zf value, the fixed-bed height Z multiplied by 1.4 could be used. For Group A powders (Type A bubbling fluidization) For this kind of particles, bubbles reach a maximum stable size, which may be estimated from the following equations: dbub 2

2 uter,2.7

(3.509)

g

ubub bub ( gdbub )0.5

(3.510)

where: 1  bub 2.5D 0.4 2.5 

for D 0.1 m for 0.1 D 1 m for D 1 m

and uter,2.7 is the terminal free-fall velocity for particles of diameter 2.7 times the actual mean particle diameter. In all equations, SI units should be used.

214


Mixing of phases in fluidized beds For the fluidization of a bed to occur, a large amount of power is initially needed, but once achieved, further increase in the feed flow rate requires much less energy. The operation of the fluidized-bed reactor is normally in the region between the packed bed and continuous-stirred tank reactor operation, due to backmixing caused by small linear velocities of the fluid. At high flow rates and low reactor diameters, almost ideal plug-flow characteristics may be achieved. Almost ideal plug-flow behavior can be achieved if high flow rates and small reactor diameters are used. The exact mode of fluidized-bed reactor operation depends on its actual design characteristics. Solids mixing The mixing of solids in fluidized-bed reactors is achieved by the entrainment of solids in the lower portion of bubbles, as well as by the shedding of these solids from the wake of the bubble (Rowe and Patridge, 1962). Thus, mixing will appear as the gas rate increases above its minimum value for the onset of fluidization. Especially, at high gas rates, by far higher than the minimum fluidizing rate, the intense circulation of the solids from the top to the bottom of the bed leads to rapid mixing of the solids. The particles that reach the top of the bed displace those found at the bottom of the bed. Practically, if the ratio of length to diameter of the bed ranges between 0.1 and 4, the solid phase can be considered to be completely mixed. The complete mixing of solids in the emulsion phase is necessary for considering the various parameters, involving the mass or volume of solids constant, throughout the reactor. That is exactly the case in the two-phase model and the Levenspiel–Kunii three-phase model. This is achieved by circulation of the solids through their entrainment by bubbles, as shown in Figure 3.61. As solids fall from the upper portions of the bed, they follow

Figure 3.61 Heterogeneous solids distribution.


215

either a plug-flow route, as assumed in Kunii–Levenspiel model, or they are considered to be in a perfect mixing state, which means that the whole emulsion phase, and consequently the gas itself, is completely mixed throughout the reactor, as assumed in the twophase model. For example, in circulating fluidized beds, for a given superficial gas velocity and solid mass velocity, several studies show that the solids are distributed in two regions in the vessel–a constant solid fraction in the lower dense region of a certain height, and a solid fraction that falls exponentially with height in an upper lean region of a certain height (Kunii and Levenspiel, 2000). Consider a first-order reaction in a fluidized bed, where we have plug-flow for the gas through the bed, while the distribution of solids across the cross-section of the bed is uniform. In general, the solution of this model, under the additional assumption of uniform solids distribution throughout the reactor, is ln

Cin f (k , Z f , us , km , Si ) Cout

(3.511)

where Si is a parameter involving the solids fraction. For simplicity, consider this parameter to be the solid fraction itself (S). If this parameter is not constant, it introduces an integral in the model: Zf

S

∫ h(S ) d z

(3.512)

o

Gas mixing in bubbling fluidization In bubbling fluidization, the mixing of gases as they travel vertically through the bed is complete. In contrast, mixing in the radial direction is extremely poor and effective distributors have to be used, especially if two gases are to be mixed. In bubbling beds, for velocities less than 5 –11 times that of ufm, the gases will move upward in both the emulsion and the bubble phases. On the other hand, for velocities higher than about 5 to 11 times that of ufm, the movement of the emulsion phase downward will carry the contained gas downward as well. The backmixing of gases increases up to a point as us/ufm is increased. After this point, the circulating or fast regime appears and the backmixing decreases with increasing velocities. It should be noted that since the solid phase is not stationary in a fluidized bed, the movement of the solid phase can also be described by a Peclet number. Thus, there are two Peclet numbers in fluidized beds for axial and radial mixing, i.e. one for the fluid side and one for the solid one. However, only the fluid-side Peclet numbers are presented here. Fluid mixing in particulate fluidization In particulate fluidization, the values of Pep are much higher than the corresponding ones for packed beds (2 –10 times) for the same value of Rep, covering the range 0.004 –0.06 for 0.4 < Rep < 100, and thus axial dispersion is more intense (Gunn, 1968).

216


The Chung–Wen correlation can be used for fixed and fluidized beds for 10—3 < Rep < 103 (Chung and Wen, 1968). f Pep

0.2 0.011Rep0.48

(3.513)

where

Rep,fm Rep

(3.514)

In this equation, Rep,fm is the particle Reynolds number based on the minimum superficial velocity for fluidization. Moreover, for fixed-beds, we can set = 1 and f = . The correlation is applicable for void fractions between 0.4 and 0.8 with particle density up to 480 lb/ft3. Note that by changing the Rep number, the fluidized bed voidage f is changed. According to Gunn (1968), the radial Peclet number in particulate fluidization (liquid–solid systems) ranges between 1 and 10 for values of Rep in the range 4–1000. Furthermore, the maximum mixing coefficient is found for f = 0.7. Finally, the lateral (radial) mixing coefficients in gas–solid fluidized beds decrease constantly (for Rep > 10) from about 10 – 0.05 by increasing the expansion ratio from 0.01 – 0.2. Gas distribution in fluidized beds The gas distributor has a considerable effect on the proper operation of the fluidized bed. Basically, there are two types (Perry and Green, 1999): (a) for use when the inlet gas contains solids and (b) for use when the inlet gas is free of solids. In the latter case, the distributor is designed to prevent the backflow of solids during normal operation, and in many cases, it is designed to prevent backflow during shutdown. To provide distribution, it is necessary to restrict the gas or the gas and solids flow so that the pressure drops across the restriction amount of 0.5 to 20 kPa. To fulfill the pressure requirements, a high velocity through the grid openings may be needed. However, values of velocity have to be below 70 m/s to avoid attrition of the solids. In industrial applications, a shroud of pipe is often installed over the opening, providing velocities as high as necessary for the pressure requirements, and at the same time enabling reduced velocities for entry to the bed, thus avoiding attrition. The technique is applied to both plate and pipe spargers. Pressure drop through a pipe or a drilled plate is given by P

f uo2 1.28 g

where: uo the velocity in the hole at inlet conditions f the fluid density in the hole at inlet conditions P the pressure drop in consistent units.

(3.515)


217

Most industrial-scale fluidized beds employ a type of jet or perforated-plate distributor (Gunn, 1968). Fluidized beds constituent parts (internals) These parts are used in fluidized beds for various purposes. For example, gas distributors and various types of baffles are installed to decrease the size of the bubbles. Moreover, draft tubes are used to enhance gas or solid circulation. Other devices such as horizontal baffles limit circulation and backmixing of solids and gas. Horizontal or vertical tubes are used for heat management. Devices used to control or improve fluidization behavior, to improve fluidization of cohesive particles or to achieve solids recovery are within the various internals met in fluidized bed reactors (Kelkar and Ng, 2002). Immersed tubes in small diameter beds may lead to slugging. Furthermore, attrition of particle breakage may change the size distribution and possibly change the fluidization behavior. Generally, the use of baffles and other mechanical means is usual in fluidized-bed reactors. These devices are largely connected with the bubbles. Specifically, the size of bubbles is decreased and thus mechanical disturbances are avoided via baffles. Furthermore, this decrease in bubble size improves gas–solid conducting. The latter can be also be achieved by packing the reactor with large particles and fluidized fine solids in the interstices of the larger ones. This type of reactor is called “fluidized-packed bed” (Gunn, 1968). However, baffles increase the overall pressure drop across the reactor length, and may also make the achievement of fluidization in all stages difficult. Finally, baffles may create pockets of low solids region, which in oxidation reactors may lead to uncontrolled burning. 3.8.3 Modeling of bubbling fluidized beds with reaction (gas–solid catalysis case and Type B Fluidization) The analysis of the following cases is based on • • • •

isothermal operation non-existence of radial gradients negligible volume changes due to reaction (R 0) rapid internal diffusion in solid particles (s 1)

Two-phase models Gas in the emulsion phase in completely mixed state In this case, the following are also assumed: (a) two-phase model applies, (b) plug-flow of the gas in the bubble phase, (c) perfect mixing of the gas in the particulate phase (emulsion phase), and (d) throughout the reactor, the gaseous reactant is assumed to pass between the bubble and particulate phase. This model is referred to as the “two-phase model”, where the emulsion phase is well mixed (Kelkar and Ng, 2002). It is a one-dimensional model, which assumes steady state (Figure 3.62). In steady state, the concentration of the reactant in the particulate phase is constant throughout the reactor because of the assumption of perfect mixing in the particulate phase.

218


Cout

Cp

Cbub, out

Particulate Phase C = Cp (-r) = f (Cp)

Bubble Phase C = Cbub

Co Ufm × A

Co (Us – Ufm ) × A

Gas flow Us × A

Figure 3.62 Two-phase model.

The model equations are the following: For the bubble phase (gas phase), fbub us

dC b Lbe (Cb Cp ) bub b (rb,vs ) 0 dz

(3.516)

where: the mass transfer coefficient between the bubble and emulsion phase (m3 gas interchange volume/m3 of reactor) (1/s) b the volume fraction of the bubble occupied by solids (–rb,vs) the reaction rate in bubbles per unit volume of solids, based on the reactant fbub the fraction of the gas flow carried by the bubble phase:

Lbe

fbub

us ufm us

(3.517)

The material balance is expressed per unit volume of reactor. For the particulate phase (dense phase), Zf

(1 fbub )QG (Co Cp ) ∫ ALbe (Cb Cp )dz AZ f (1 bub ) eS (rp,vs ) 0 0

(3.518)


219

where: eS Co A Zf QG

the volume fraction of the dense phase occupied by solids (eS1fm) the feed concentration of the gas the cross-sectional area of the bed the fluidized-bed height the volumetric flow rate of gas through the reactor.

Gas in the emulsion phase in plug flow In this case, the following are assumed: (a) two-phase theory applies (bubble and particulate phases), (b) plug flow of gas throughout the reactor, i.e. in the bubble and emulsion phase. This model is referred to as the “twophase model” with plug emulsion phase (Kelkar and Ng, 2002). For the bubble phase (gas phase), fbub us

dC b Lbe (Cb Cp ) bub b (rb,vs ) 0 dz

(3.519)

where: Lb in (m3 gas interchange volume/m3 of reactor) (1/s) b the volume fraction of bubble occupied by solids (–rb,vs) the reaction rate in bubbles per unit volume of solids, based on the reactant fbub the fraction of the gas flow carried by the bubble phase The material balance is expressed per unit volume of reactor (Figure 3.63). For the particulate phase (dense phase), (1 fbub )us

dC p dz

Lbe (Cb Cp ) (1 bub )(1 mf )(rp,vs ) 0

(3.520)

Vtot = 1

Vgas, e = εfm × Ve Vbub = εbub

Ve = 1 – εbub

Vsolids, e = (1 – εfm) × Ve

Vsolids, e = (1 – εfm) (1 – εbub)

Figure 3.63 Volume fractions in a two-phase fluidized bed (where e denotes the emulsion phase).

220


As in the case of two-phase fixed beds and trickle beds, the material balances of fluidized beds are written in terms of reactor volume (VR) (see Section 3.1.1 for derivation) and the following relationship holds: (R)

V 1 dN VS (rvs ) L (ru ) VR dt VR VR

(3.521)

For the bubble phase, VL bub VR

(3.522)

VS Vbubble VS bub b VR VR Vbubble

(3.523)

Then, the overall rate of reaction (–R) per unit volume of reactor is (R)

1 dN bub b (rvs ) bub (ru ) VR dt

(3.524)

For the emulsion phase, VL Vemulsion VL

(1 bub ) fm VR VR Vemulsion

(3.525)

VS Vemulsion VS (1 bub )(1 fm ) VR VR Vemulsion

(3.526)

Then, the overall rate of reaction (–R) per unit volume of reactor is (R)

1 dN (1 bub )(1 fm )(rvs ) (1 bub ) fm (ru ) VR dt

(3.527)

Kunii-Levenspiel three-phase model (bubbling bed model) In the following, a simplified version of the Kunii–Levenspiel model is presented (Levenspiel, 1962). This design model follows the assumptions of the bubbling bed one. According to this hydrodynamic model, the solids in the emulsion phase flow smoothly downward (plug-flow of solids). The entire emulsion phase is thoroughly agitated, whereas the emulsion exists at minimum fluidizing conditions. The gas occupies the same void fraction in this phase as it would in the entire bed at minimum fluidization. Furthermore, the reaction takes place wherever the solid exists, i.e. in the bubbles, in the wakes, and in the emulsion phase. In Figure 3.64, the model is schematically presented.


221

CAout

Bubble phase

cloud and wake

Emulsion

δ

βc + αδ

1– δ –βc

Zf

ubub Kbc

CAo

Kce

us

Figure 3.64 Levenspiel model (simplified Kunii–Levenspiel model).

Material balances can be written over a differential section of the bed (dz) for a reactant, in each of the three-phases (bubble, cloud, and emulsion). Then, the equations of the model are as follows: For the bubble phase (gas phase), b DL,b

d 2 Cb dz

2

ubub

dCb K bc (Cb Cc ) b (rb,vs ) 0 dz

(3.528)

where Kbc is the mass transfer coefficient between the bubble and cloud phase in (m3 gas interchange volume/m3 of bubble) (1/s). For the cloud phase (cloud and wake),   dCc uc  c K bc (Cb Cc ) K ce (Cc Ce ) c (rc,vs ) 0   dz

(3.529)

where Kce is the mass transfer coefficient between the cloud and the emulsion phase in (m3 gas interchange volume/m3 of bubble) (1/s). For the emulsion phase (dense phase),  1  dCe K ce (Cc Ce ) e (re,vs ) 0 ue    dz

(3.530)

where uc and ue are the velocities of the gas in the cloud and emulsion phases, respectively. The material balance is expressed per unit volume of bubble phase.

222


Note that in this case, as in the case of slurry reactors, the material balances are based on the unit volume of the fluid (bubble) phase. The relationship between the rate expressions is (see Section 3.1.1 for derivation) (ru )

1 dN VS (rvs ) VL dt VL

(3.531)

For each one of the three-phases in the fluidized bed (i b, c, or e), VS i VL

(3.532)

Then, the overall rate of reaction (–ru) per unit volume of liquid is (ru )

1 dN i (rvs ) VL dt

(3.533)

Apart from the basic assumption of the corresponding hydraulic model, the following approach is valid for a fairly high gas flow rate. The gas flow in both the emulsion and the cloud volume becomes so small that the flow of gas in these regions can be neglected. Consequently, flow through the bed occurs only in the bubble phase (Levenspiel, 1972). This means that fbub = 1 or in more practical terms, us – ufm ufm. This is a real situation in industrial catalytic reactors, which are characterized by high gas velocities, much higher than the minimum fluidization velocity (Werther, 1980). The Kunii-Levenspiel model can be simplified by assuming that the derivative terms of eqs. (3.529) and (3.530) are unimportant compared to the rest of the terms. Furthermore, plug flow can be assumed for gas (bubble) phase. Under these assumptions, the set of equations reduces as follows (Carberry, 1976; Fogler, 1999): For the bubble phase (gas phase), ubub

dCb K bc (Cb Cc ) b (rb,vs ) 0 dz

(3.534)

dCb (rb,u )overall 0 dz

(3.535)

or ubub

The overall rate of reactant disappearance in the gas phase is (rb,u )overall b (rb,vs ) K bc (Cb Cc )

(3.536)

where –rb,vs is the reaction rate per unit volume of solids and (–rb,u)overall the reaction rate in bubbles per unit volume of bubbles.


223

For the cloud phase, K bc (Cb C c ) K ce (Cc Ce ) c (rc,vs )

(3.537)

For the gas in the emulsion phase (dense phase), K ce (Cc Ce ) e (re,vs )

(3.538)

3.8.4 Liquid–solid particulate fluidization (liquid–solid catalysis, adsorption, and ion exchange) Three models of the fluidized-bed process can be considered (Menoud et al., 1998): • • •

complete backmixing of the liquid and the solid (CSTR model); near plug flow of the liquid and a complete backmixing of the solid, which is the plugflow model with backmixing of the solid; near plug-flow of the liquid with no backmixing of the solid, which is the plug-flow model.

Since the particulate flow regime is characterized by a relatively uniform expansion of the bed without the formation of bubbles, it can be approximated as an expanded fixed bed (Wen, 1984; Hopper, 2001). Thus, the fixed bed models can be used for the particulate flow regime in fluidized beds for catalysis as well as for adsorption and ion exchange. Fluidized-bed operation in adsorption and ion exchange is rare and has been studied for heavy metals removal from the liquid phase to a chelating resin, and for the determination of mass transfer rates in a magnetically stabilized liquid fluidized bed of magnetic ionexchange particles (Menoud et al., 1998; Hausmann et al., 2000). The main mass transport resistance in liquid fluidized beds of relatively small particles lies in the liquid film. Thus, for ion exchange and adsorption on small particles, the mass transfer limitation provides a simple liquid-film diffusion-controlled mass transfer process (Hausmann et al., 2000; Menoud et al., 1998). The same holds for catalysis. It should be noted that in the material balances, the fluidized bed voidage should be used instead of fixed-bed voidage, and that the appropriate mass transfer correlations should be used for particulate fluidization. 3.8.5 External mass transfer In fluidized beds, mass transfer involves two different mechanisms. The first one is the ordinary mass transport between the fluid and the solid. The treatment of this type of transport is quite similar to fluid–solid mass transfer found in other types of operations such as fixed beds and agitated tank reactors. This mechanism of mass transfer is not always significant in fluidized beds and can be totally neglected in some cases. In contrast, the second mechanism is quite different and it is found only in fluidized-bed reactors. It concerns the mass transfer between the bubbles and the particulate phase

224


through an intermediate phase called “cloud” and cannot be overlooked, at least in most of the fluidized beds. Fluid–solid mass and heat transfer in particulate fluidization In fluidized beds, the gas bubbles in combination with the high heat-transfer area per unit mass of catalyst, lead to mixing in the emulsion phase, and in turn to temperature uniformity throughout the reactor (Smith, 1981). Considering that the same large area is also available to mass transfer, one can easily understand why the temperature and concentration gradients between the fluid and particle surface are usually negligible. It should be noted that the mass and heat transfer coefficients in fluidized beds are less than those in fixed beds, where large differences in temperature and concentration between the fluid and the solids exist. However, the area is much higher in fluidized beds as a result of the use of smaller particles, which leads to higher products of transfer coefficient and area. Note that the mass transfer is a function of the transfer coefficient and the available surface. The mass and heat transfer coefficient between the gas or liquid phase and the solid phase can be evaluated using the Chu–Kaiil–Wetterath correlation (Smith, 1981): 0.44

 dp G  jD or jH 1.77    (1 f ) 

(3.539)

This equation is valid within the limits 30

dp G

(1 f )

5000

and kf  am    G  at   Df 

23

h  a   cp  jH f  m   cp G  at   f 

23

jD

(3.540)

(3.541)

where: f G at am Df hf cp f

the void fraction of the fluidized bed us, the fluid mass superficial velocity, kg/m2 s the total mass transfer area the effective mass transfer area the solute diffusivity in the fluid phase the heat transfer coefficient, W/m2 K the specific heat capacity of the fluid, J/kg K the thermal conductivity of the fluid, W/m K.

At temperatures above 400 °C and for large particles, heat transfer by radiation should be also taken into account.


225

If a fluidized and a fixed bed are operated at the same Rep, the mass transfer coefficient is higher in the latter. However, the fixed bed can be operated only in downflow mode, because the solids would be entrained in upflow, at high Reynolds numbers. Rep should be lower than the value of 1.8 times the minimum Rep for fluidization to avoid excessive attrition of the particles in the fluidized bed. For the system shown in Figure 3.65, the minimum Rep for fluidization is about 8, and thus, the upper limit for the fixed-bed operation in downflow mode is lower than 13. Furthermore, high residence times and thus low superficial velocities and low Rep are met in fixed beds. For example, Rep values lower than 8 are typical in ion exchange and adsorption from liquid phase in fixed beds. For the system shown in Figure 3.65, if the Rep in the fixed bed is lower than 8 but is higher than this value in the fluidized bed, the fluidized-bed mass transfer coefficient is higher than that in the fixed bed. For 5 Rep 100, the following correlation, obtained by Rahman and Streat for mass transfer, is valid for conventional liquid fluidized beds (Rahman and Streat, 1981; Hausmann et al., 2000): 0.86 0.5 1 3 Sh Rep Sc (3.542) f For lower Reynolds numbers (0.22 Rep 1), the Koloini–Sospic–Zumer correlation is more accurate (Koloini et al., 1977; Hausmann et al., 2000). Sh

0.7 Rep Sc1 3 f

(3.543)

Here, the Rep is based on superficial velocity. 0.018 Smith, Fixed Bed

0.016

Smith, Fluidized Bed 0.014

Wilson-Geankoplis

kf (cm/s)

0.012 0.01 0.008 0.006 0.004 0.002 0 1

10

100

Rep

Figure 3.65 Comparison of mass transfer coefficients for fixed and fluidized beds (system: water at 20 °C, S 1, h 2.08 g/cm3, 0.4, Df 105 cm2/s, and dp 1 mm).

226


Bubble–particulate phase mass transfer (bubbling fluidization) Following the bubbling bed model of Kunii–Levenspiel, the mass transfer coefficient of gas between the bubble and the cloud is (Levenspiel, 1972; Fogler, 1999) Dg1 2 g1 4 ufm K bc 4.5 5.85 5 4 dbub dbub

(3.544)

where the units of Kbc is (m3 gas interchange volume/m3 bubble)(1/s) and Dg is the diffusion coefficient of the solute in the gas phase. Here, CGS units are used. A typical value of Kbc is 2 s1. The mass transfer coefficient of gas between the cloud and the emulsion is  fm Dg ubub  K ce 6.78   3   dbub

1 2

(3.545)

where the units of Kce is (m3 gas interchange volume/m3 bubble)(1/s). CGS units are also used in this relation. A typical value of Kce is 1 s1. Here, both mass transfer coefficients are based on bubble volume. Another useful expression in reactor modeling is that of a mass transfer coefficient based on reactor volume: L K bub

(3.546)

where: bub the volume fraction of the bubble phase, m3 bubbles/m3 reactor L the mass-transfer coefficient based on the reactor volume, (m3 gas interchange volume/m3 of reactor) (1/s) K the mass-transfer coefficient based on the bubble volume, (m3 gas interchange volume/m3 of bubble) (1/s) Mass transfer coefficients are also expressed in m/s. These units are related to the other ones as follows (Kelkar and Ng, 2002): km

L

$L km bub

(3.547)

K $K km obub o abub

(3.548)

bub

and km where: km L abubo

the mass transfer coefficient, m/s or in the equivalent unit of (m3 gas interchange volume/m2 of bubble interfacial area) (1/s) the mass transfer coefficient, (m3 gas interchange volume/m3 of reactor) (1/s) the interfacial area of the bubble phase per bubble volume (m2 bubble/m3 bubble)

3.9 Particle Analysis

227

abub the interfacial area of the bubble phase per reactor volume, m2 bubbles/m3 reactor, estimated by (Kelkar and Ng, 2002) abub

6 bub dbub

(3.549)

and o abub

6

(3.550)

dbub

On the basis of these relationships, Kelkar and Ng (2002) presented the following relationships for km: Bubbling bed model Mass transfer between bubble and cloud: Dg1 2 g1 4 3 kbc ufm 0.975 1 4 4 dbub

(3.551)

where the unit of kbc is in m/s. Mass transfer between cloud and emulsion:  4 fm Dg ubub  kce   dbub 

1 2

(3.552)

where the unit of kbc is in m/s. Two-phase model Mass transfer between bubble and emulsion:  4 fm Dg ubub  u kbe fm  3  dbub 

1 2

(3.553)

where the unit of kbe is in m/s. 3.9 PARTICLE ANALYSIS In this section, various issues concerning solid particles are presented. The analysis covers the most important particle properties (surface area, particle shape and size distribution, mechanical strength, and density) as well as the behavior of a single particle in suspension (terminal velocity) and of a number of particles in fluidization state. Finally, the diffusion of molecules in a porous particle (diffusion coefficients) is also discussed. This “particle analysis” is of paramount importance when dealing with heterogeneous operations like adsorption, ion exchange, and catalysis.

228


3.9.1 Surface area The solid provides an extended surface to the reaction or adsorption to take place. The area provided by the solid is the sum of the exterior and interior ones. Consequently, the solid surface includes not only the geometrical one as determined from the solid’s shape but also the interior surface that is the result of its porous structure. The surface area is expressed as specific surface area in units of m2/g. Its value may be from a few m2/g up to hundreds of m2/g. In the case of a porous solid, the interior surface constitutes the greatest percentage of the total surface, and high values of specific surface area may be achieved. Specifically, the specific area of an activated carbon can reach the value of 1500 m2/g. So, the available area for a hydrocarbon to react on 4 g of activated carbon is equal to that of a football field. 3.9.2 Particle shape Sphericity is a measure of how close a particle is to being a sphere and is defined as S

surface area of a sphere with same volume as the particle actual surface area of the particle

(4.554)

For granular particles, typical values of sphericity range between 0.6 and 0.95. In Table 3.13, the sphericity of selected materials is presented. 3.9.3 Particle size The size (diameter) and the shape of the solid particles affect the mass transfer phenomena within the particle, and thus the effectiveness in the case of a catalyst. The following shapes and sizes are frequently used in applications: • • • •

20–100 µm diameter spheres for fluidized-bed reactors 0.3–0.7 cm diameter spheres for fixed-bed reactors 0.3–1.3 cm diameter cylinders with a length-to-diameter ratio of 3–4 up to 2.5 cm diameter hollow cylinders or rings

When particles of irregular shape are involved, the particle diameter corresponding to a sphere of the same volume dsph is used in many equations. For instance, a cubic particle with a side of 5 mm has a volume of 53, and thus the dsph is 3

4  dsph  Vsphere 125  $dsph 6.20 mm 3  2  However, in the typical case of an irregular-shaped particle, it is not easy to calculate its volume and thus dsph is taken equal to the mean nominal diameter measured by sieve analysis dp. In the present book, dp is considered to be equal to the average sieve diameter.


229

Table 3.13 Sphericity of selected materials (McCabe et al., 1983; Perry and Green, 1984 and 1999; Broadhurst and Becker, 1975) Material

Sphericity

Spheres Iron shot Clover seed Cracking catalyst Ottawa sand Tungsten powder Short cylinder (length = diameter) Rounded sand Cube Sand (average for various types) Coal dust Cork Flint sand (jagged) Coal dust (natural and up to 3/8 in) Crushed glass Most crushed materials Wilcox sand (jagged) Raschig rings Berl saddles Mica flakes

1 0.95 0.95 0.95 0.95 0.89 0.87 0.83 0.81 0.75 0.73 0.69 0.65 0.65 0.65 0.6–0.8 0.6 0.33–0.58 0.3 0.28

For polydispersed beds consisting of particles having different diameters, the equivalent diameter deq should be preferably used (Pavlov et al., 1979): deq

1 w ∑ di i 1 n

(3.555)

where: n the number of fractions di the average screen size of the i-th fraction wi the mass content of the i-th fraction in the particle mixture. The average screen size of a fraction is the mean arithmetical value of the average size of the screen mesh through which a fraction passes and the mesh that retains the fraction. Finally, the United States standard screen series is used (mesh size) for expressing the particle size. In Table 3.14, the conversion from mesh size to (cm) is given. 3.9.4 Pore structure Beyond the surface area and the pore volume, the distribution of the pore radii of a catalyst has to be known, since the pore radius allows or does not allow a molecule to move

230


Table 3.14 Conversion from mesh to cm for particle radius Mesh

Particle radius (cm)

5 10 16 20 30 40 50 60 70 80 100 120 140 170 200 230 270 325

0.2 0.1 0.06 0.042 0.030 0.021 0.015 0.013 0.011 0.0089 0.0075 0.0063 0.0053 0.0044 0.0037 0.0031 0.0027 0.0022

through the pores depending on its size. Consequently, a portion of the surface area may not be available to reactants, or generally, to the diffusing species. As discussed earlier, most of the solids used in catalysis, adsorption, and ion exchange, which exhibit large surface areas, are porous. Porosity is a term used to describe the pore space of a material, defined as the fraction of the bulk volume that is occupied by pore or void space. Void is the space or interstices between particles. The individual pores may vary greatly in both size and shape within a given solid, and between one solid and another. As presented in Chapter 2, the pores can be classified as (a) macropores, for diameters above 50 nm, (b) mesopores, for diameters in the range 2–50 nm, and (c) micropores, for diameters below 2 nm. It has to be mentioned that in a solid, a wide and continuous range of pore sizes is found, from macropores to micropores. A pore can also be an open pore, seen as a cavity or channel communicating with the surface of the particle, or a closed pore that is not connected to the surface. The total pore volume is the total internal volume per unit mass of catalyst. Some of this pore volume may be completely enclosed and thus not available to the reaction molecules. Moreover, the accessible pore volume changes with the size of the diffusing molecules. The pore-size distribution is the distribution of pore volume with respect to pore size (Figure 3.66). It is an important factor controlling the diffusion of reactants and products in the porous solid and thus an essential property for its characterization. The computation of pore size distribution involves a number of assumptions, and therefore reporting of the data should always be accompanied by an indication of the method used for its determination. An interesting phenomenon about adsorption of gases on solids and ion exchange of ions on resins is swelling. Some porous solids expand on exposure to the vapors of adsorptives.


231

0.0040 0.0035 0.0030

dV/dD

0.0025 0.0020

mesopores

0.0015

macropores

0.0010 0.0005 0.0000 0

20

40 60 80 pore diameter D (nm)

100

120

Figure 3.66 Pore size distribution of an automotive catalyst.

Rigid adsorbents, like charcoal or silica gel, swell by only a small percent, but nonrigid adsorbents, like agar–agar, may swell to several times the original size. Provided the adsorption occurs slowly, the rigid solid may accommodate the stresses, but if suddenly exposed to high vapor pressure the material may shatter. 3.9.5 Mechanical strength The mechanical strength of a catalyst is really important in its commercial applications, since broken pieces and losses can lead to a decrease in catalytic activity and a significant expense, especially when precious metals are used as the catalytic agents. Mechanical strength is equally important in adsorption and ion exchange, especially in fixed-bed operations. Crush strength is defined as the resistance of a formed particle to compressive forces. Measurements of crush strength provide an indication of the ability of the particle to maintain its physical integrity during handling and use. When crush strength is measured for single pieces, it is called “piece crush strength,” and when it is measured for a bulk sample it is called “bulk crush strength.” Other terms that have been used for piece crush strength but which are not recommended are static crush strength and single-pellet crush strength. The loss of solid during handling or use is called “attrition.” Abrasion, grinding, or rubbing of the particles with each other or with container walls can lead to attrition.

3.9.6 Density In this section, definitions of terms used in relation to particle density are presented. As will be shown, these issues exhibit many alternative approaches. For instance, density is

232


generally defined as mass divided by volume. While mass is an easily measurable quantity, it is not the case for volume. So, referring to a porous particle, which is its volume and how can it be estimated? What should be done with the pores of the particles? Depending on the how this volume is specified, there are various types of density. Definitions A porous particle contains many interior voids known as open or closed pores. A pore is characterized as open when it is connected to the exterior surface of the particle, whereas a pore is closed (or blind) when it is inaccessible from the surface. So, a fluid flowing around a particle can “see” an open pore, but not a closed one. There are several densities used in the literature and therefore one has to know which density is being referred to (Table 3.15). True density may be defined as the mass of a powder or particle divided by its volume excluding all pores and voids. True density is also referred to as absolute density or crystalline density in the case of pure compounds. However, this density is very difficult to be determined and can be calculated only through X-ray or neutron diffraction analysis of single-crystal samples. Particle density is defined as the mass of a particle divided by its hydrodynamic volume. The hydrodynamic volume includes the volume of all the open and closed pores. Practically, the hydrodynamic volume is identified with the volume included by the outer surface of the particle. The particle density is also called “apparent” or “envelope” density. The term “skeletal density” is also used. The skeletal density of a porous particle is higher than the particle one, since it is the mass of the particle divided by the volume of solid material making up the particle. In this volume, the closed pores volume is included. The interrelationship between these two types of density is as follows (ASTM, 1994; BSI, 1991): s

p 1 p

p

b 1

(3.556)

(3.557)

where: p b p

the internal particle porosity the bed density the bed porosity the particle density.

Bulk density, or packing density, includes all pores and voids (interparticle spaces) in its calculation. This value depends on the form of the particle (powder, tablets, or extrudates) and the packing procedure. It is extensively used in reactor designing since this value connects the solid volume with that of the reactor. When a porous particle is immersed in a fluid, it has an effective density different from the skeletal and particle density. This density is termed “wet” or “effective density” (Perry and Green, 1999). h p f s (1 p ) s p (s f ) p f p

(3.558)


233

Table 3.15 The density definitions Density

Bulk Particle Skeletal True

Volume included in definition Symbol

Solid material volume

Open pore volume

Closed pore volume

Interparticle void volume

b p s t

√ √ √ √

√ √ √

√ √

√

where f is the fluid density. In the present book, the term “hydraulic density” is introduced in order to highlight the use of this kind of density in hydrodynamic calculations involving suspension of particles. Despite the fact that this hydraulic density is essential to many calculations involving fluidization and the suspension of particles, it is characteristic that in the related literature, authors use the terms “particle density” or “solid density” without specifying if the fluid in the pores has been taken into account. However, the subject of hydraulic density has been analyzed in studies of the behavior of impermeable aggregates in fluids. As these aggregates could be seen as porous particles, the relevant analysis is interesting and will be presented here. The case of hydraulic density The case of impermeable spherical aggregates has been analyzed by Johnson et al. (Johnson et al., 1996). In this work, the settling velocity of aggregates in liquids using Stoke’s law has been studied and a modified Stoke’s law has been introduced. The difference from the classical Stoke’s law is that the density difference is expressed in terms of the aggregate density. a a f s (1 a ) where:

(3.559)

a the aggregate porosity a the aggregate density s the density of the particles composing the aggregate.

Thus, the aggregate–fluid density difference becomes a f a f s (1 a ) f s (1 a ) f (1 a ) (1 a )(s f )

(3.560)

To highlight the difference from the classical Stoke’s equation, the density difference in the latter is (sf), where s is the skeletal density of the rigid particle. Now, it is easy to illustrate the analogy to a porous particle. A porous particle can be viewed as an aggregate,

234


where s is the density of the solid part (skeletal density) and a is the porosity of the particle. Then, it is clear that Johnson’s aggregate density is the hydraulic density defined in eq. (3.558), since the term s(1 – a) is equal to the particle density p. Several forms of the modified Stoke’s law have been used in a series of studies concerning aggregates in the liquid phase (Li and Yuan, 2002). In these studies, the particle–liquid density difference has been further modified and adopted for the cases of impermeable biological/microbial aggregates, permeable aggregates, and fractal aggregates. The introduction and use of a hydraulic density, termed in a different way, in liquid–porous solid fluidization has been done by Nesbitt and Petersen (1998). They point out that for resins, which are porous in nature, it might be more correct to use an “apparent density” of fluidization (ap), a property relevant only when the resin is in a suspension, with the fluid phase intruding into the pores. However, the authors did not use eq. (3.558), but an experimental technique, measuring the terminal velocity of the resin particles and evaluating the “apparent density” using the Shiller and Naumann terminal velocity model: Ga 18 Reter 2.7 Re1.687 ter

(3.561)

where: Ga

dp3 gf (ap f )

Reter

2f f dp uter

(3.562)

(3.563)

for 3.6 Ga 10 As in Stoke’s equation, the solid–fluid density difference is also involved in the Shiller and Naumann model and so the evaluated density is equivalent in nature to the hydraulic density defined above by Johnson, which is different from skeletal and particle density. The benefit in this case is that the experimental determination of particle voidage and particle density, which could be quite complicated in some cases, is avoided and the hydraulic density is directly evaluated without using eq. (3.558). The same approach has been followed by Menoud et al. (1998) in a study of a fluidized bed using a chelating resin. Wet density has been used by Feng et al. (2003) for a fluidized-bed ion-exchange system. Finally, Griffith et al. (1997) used a gravimetric method for the evaluation of the so-called “effective particle density during fluidization.” The particle and bulk densities are commonly used in mass balance equations, since the mass and the external volume of the particles are involved. On the other hand, the hydraulic density should be preferably used in hydrodynamic calculations, because buoyancy forces are involved, and so the total mass of the particle should be taken into account, including the fluid in the open pores. It is obvious that the particle density is equal to the skeletal and hydrodynamic density in the case of nonporous particles. Moreover, in the case of a porous solid in a gas–solid system, the gas density is much lower than the particle density, and thus


235

the latter is practically equal to the hydraulic density. This is the reason why in gas–solid fluidization, the particle density is used in all hydraulic calculations. The complication is found only in the case where porous solids are found in suspension in a liquid–solid system, e.g. fluidization and suspension/sedimentation in agitated vessels. In any case, one should be aware of the type of density used or that should be used. For example, Menoud et al. (1998) used two different densities for modeling the fluidized bed operation, i.e. the resin particle density for mass balances and relevant calculations, and the hydraulic density for the relevant hydraulic calculations (e.g. for the evaluation of Archimedes number). Special reference should be made to resin–liquid systems, where the phenomenon of swelling makes the case more complex. A resin’s matrix is flexible and when immersed in a liquid, its volume expands, leading to an increase in its particle diameter and in turn, to a decrease in particle density (mass of dry resin per volume of swollen particle). Furthermore, the loading of the resin with ions results in further changes in its volume (Helfferich, 1995). Thus, in these cases, the particle density and diameter as well as the hydraulic density should be referred to for the swollen and loaded resin. In practice, a mean value is frequently used. 3.9.7 Terminal velocity of a single particle The forces that are present when an isolated porous particle is suspended in a fluid are the following. The gravitational force Fg, Fg M p g

(3.564)

Fb Vf g

(3.565)

the buoyant force Fb,

and the drag force Fd. Fd Fg Fb

f Apr CD 2

2 uter

(3.566)

where: Mp the mass of the particle Vf the volume of fluid displaced by the particle Apr the area of the particle projected on a plane normal to the direction of flow (projected area perpendicular to flow) uter the terminal velocity CD an empirical drag coefficient. Then, M p pVp p f Vp

(3.567)

236


and Vf f Vp

(3.568)

In eq. (3.564), the gravitational force accounts for the mass of the fluid, which is included in the volume of the particle. So, M p Vp ( p p f ) hVp

(3.569)

Using the above equations, the terminal velocity relationships can be derived. The drag force is Fd ( h f )Vp g

(3.570)

Finally, a formal definition of terminal velocity is derived from the equations above:  2 gVp ( h f )  uter    Apr f CD 

0.5

(3.571)

For a spherical particle,

Vp Apr

 dp3     6   dp2     4 

2 dp (3.572)

3

Note that the “projected area” is the area of the object projected on the perpendicular to the flow plane; for a sphere, this is equivalent to the area of a circle with the same radius. Thus, the following well-known definition is derived (Perry and Green, 1999):  4 gdp ( h f )  uter   3f CD  

0.5

(3.573)

where CD is the drag coefficient. This is the constant terminal velocity with which a particle falls when the accelerating effect of gravity balances the drag force. Within the intermediate Reter region (0.1 < Rep < 1000), the drag coefficient can be estimated via the relation CD

24 1 0.14 Rep0.7 Rep

(3.574)


237

Within the range of Stoke’s law, Reter 1 and CD 24/Reter, and thus uter

gdp2 ( h f )

(3.575)

18 f

Within the range of Newton’s law, Reter 1000 and CD 0.445, and thus  gdp ( h f )  uter 1.73   f  

0.5

(3.576)

where Reteris the Reynolds number based on the terminal particle velocity. In all the equations above, SI units should be used. For us uter, the particle is blown out of the bed and thus it can be considered as the maximum fluidization velocity. Haider and Levenspiel (1989) found a useful relationship for the direct evaluation of the terminal velocity of particles. They used the following equations on the grounds that 0.5 S 1:  18 2.335 1.744 S  U ter  2  0.5 Dsph  Dsph 

1

(3.577)

where: 1 3

  f2 U ter uter    g( h f ) 

(3.578)

and 1 3

 g( h f )  Dsph dsph  f 

2  

(3.579)

SI units should be used in these equations. Furthermore, the effect of particle sphericity is included. Here, dsph could be taken equal to the mean nominal diameter measured by sieve analysis (dp). Another useful correlation is the Khan–Richardson correlation (Hilal, 2000): 0.27 0.64 3.45 Ar 2.07 Reter 0.33 Reter

(3.580)

where Reter is the Reynolds number based on the terminal particle velocity and Ar is the Archimedes number. Ar

dp3 f ( h f )g

2

(3.581)

238


This correlation holds for both gas and liquids and for the whole region of Reynolds number. However, since it cannot be solved analytically, a trial-and-error procedure is required. 3.9.8 Particles in fluidization state In a fluidized bed of depth Z and cross-sectional area Ab, the total number of particles (Ntot) can be evaluated as follows: N tot Vp (1 )ZAb N tot

(1 )ZAb Vp

(3.582)

The number of particles in a unit cross-sectional area of bed N is N

N tot (1 )Z Ab Vp

(3.583)

The total weight of the suspension is B NAb M p g f ZAb g

(3.584)

An overall force balance on a control volume (solids and fluid) of a fluidized bed reveals that the total weight of the suspension is supported by the net force due to the difference in the pressure intensity on the horizontal surfaces (Foscolo and Gibilaro, 1984). pI pI ( z ) pI ( z Z )

B NM p g f Zg Ab

(3.585)

The effective mass of the particle is (eq. (3.569)) M p Vp ( p p f ) hVp

(3.586)

and NM p

(1 )Z hVp (1 )Z h Vp

(3.587)

Then pI f (1 ) h Zg

(3.588)


239

The pressure intensity is related to the piezometric pressure p, through the definition of the latter quantity: p pI f gz

(3.589)

where (z) is the elevation above a datum plane. Then p p( z ) p( z Z ) pI + f gz f g( z Z ) pI f gZ

(3.590)

By using the above equations, the eq. (3.448) for the determination of pressure drop in a fluidised bed is derived (Section 3.8.2): p Zgf f (1 ) h

(3.591)

p Z (1 )( h f )g

(3.592)

or

The same analysis could be conducted by using forces. Consider a particle of height l and of projected area perpendicular to flow Apr. The net upthrust on the projected area dApr is pI d Apr f (1 ) h gl d Apr

(3.593)

The integration of this equation over the entire projected area of the particle yields the buoyancy force: Fb f (1 ) h g

∫

l d Apr

Apr

(3.594)

The integral in this equation is equal to the volume of particle. Thus, Fb f (1 ) h gVp

(3.595)

Comparing this equation to that of a single particle (eq. (3.565)), it is evident that in applying the Archimedes principle to a particle in a fluidized suspension, it is an average suspension density, including the particle density, rather than that of the fluid alone, that determines the buoyancy force (Foscolo and Gibilaro, 1984). The gravity force is Fg hVp g

(3.596)

Fd ( h f )Vp g

(3.597)

Thus, the drag force is

240


The pressure drop reflects the sum of single-particle drag-force contributions, which is (Foscolo and Gibilaro, 1984) p NFd

(3.598)

Using this equation and the equations for Fd and N, p Z (1 )( h f )g

(3.599)

From the analysis presented in the last two paragraphs, it is evident that the gravitational force acting upon the particle is used for the derivation of the equations for the terminal velocity and the pressure drop in the fluidized bed. Then, it is clear that the hydraulic density should be used in these equations as well as in any other equations that are derived from a similar force-balance analysis. For instance, this is the case of the Foscolo–Gibilaro criterion for determining the fluidization pattern (Section 3.8.2). As mentioned before, the hydraulic density is not used for gases, simply because the density difference between the solid and the fluid phase is so big that this density is practically equal to the particle density. For this reason, the particle density is found in all relevant equations and correlations. Much attention should be given to correlations for liquid–solid suspensions or fluidizing systems derived experimentally. If the experimental data have been correlated to particle density, this kind of density and not the hydraulic density should be used. For instance, this is the case of the Liu–Kwauk–Li criterion for determining the fluidization pattern (Section 3.8.2). However, for correlations that have been derived using nonporous particles, the hydraulic density should be used. This is because the correlation accounts for the whole mass included in the volume of the particle, which is the sum of the solid mass and liquid mass in the pores for porous particles. A problem arising when using hydraulic density is the possibility of partial internal wetting of the porous particle. Using eq. (3.358), it is assumed that the pores are totally filled with liquid, which is generally, but not always, true. This is why several authors correlate data to particle density, which normally, is a given and well-defined parameter. Furthermore, for the same reason, some authors use models to indirectly determine hydraulic density. 3.9.9 Diffusion and diffusion coefficients in porous solids General In porous solids, it is the diffusion within the solid particles that usually controls the mass transfer in many applications, such as adsorption, ion exchange, drying, and heterogeneous catalysis. Bulk diffusion is considered to take place within the large pores, except that it is hindered by the pore walls (Perry and Green, 1999). This hindrance is expressed by the tortuosity factor p, which is estimated from geometric arguments. However, this approach often results in values far from reality. Hence, a diffusion model fed with experimental measurements is generally employed for the evaluation of the effective diffusivity Deff and


241

hence p. Typical values of the tortuosity factor are in the range 2 < p < 6 for silica gel and alumina, 5 < p < 65 for activated carbon, and 3 < p < 4 for catalyst particles, if no other information is available (Froment and Bischoff, 1990). Generally, the tortuosity factor should be handled as an empirical constant, which is characteristic for each material, and should be evaluated experimentally (Perry and Green, 1999). Estimation methods Depending on the specific pore system, the diffusion in a solid particle takes place in three ranges (Perry and Green, 1999): • • •

Knudsen diffusion in small pores (Dk) Fick’s or molecular diffusion in large pores (DF), also called “bulk diffusion” Transition range diffusion, which includes both mechanisms (Dt)

The Knudsen diffusion coefficient Dk corresponds to the movement of gaseous solutes in small pores and can be estimated by using the following equation (Perry and Green, 1999): Dk 9700rpore

T MB

(3.600)

Here, Dk is in cm2/s, rpore the average pore radius (cm), MB the molecular weight of the gas (g/mol), and T the temperature in K. In the case of a narrow pore size distribution, the average pore radius can be estimated as follows (Froment and Bishoff, 1990): rpore

2 p Ss p

(3.601)

where p is the internal porosity of the solid, p the particle density of the solid (g/cm3), and Ss the specific area of the solid (cm2/g). The bulk diffusion coefficient corresponds to the movement of gaseous or liquid solutes in large pores and can be estimated as follows: DF = Df

p p

(3.602)

where Df is the diffusion coefficient of the molecules in the fluid phase and p the tortuosity of the solid structure, which is defined as the ratio of the actual distance a molecule travels between two points and the shortest distance between these points (Fogler, 1999). Fogler (1999) adds one more factor to the above equation: DF Df

p p p

(3.603)

where p is the constriction factor that accounts for the variation in the cross-sectional area that is normal to diffusion. A typical value of the constriction factor is 0.8.

242


Then, the combined (transitional) diffusion coefficient is Dt

1 1 DF Dk

(3.604)

The tortuosity could be evaluated using the Bruggemann’s equation: p

1

(3.605)

p

or using the Mackie and Meares equation (Perry and Green, 1999), p

(2 p )

(3.606)

p

For gel-type exchange resins and for macropore resins with a low degree of crosslinking, the following equation is proposed for the solid diffusion coefficient (Helfferich, 1962):  p  Ds Df    2 p 

2

(3.607)

which is Fick’s or molecular diffusion in large pores, as defined before. Although there are a lot of data in the literature regarding diffusion coefficients in liquids or their calculation from molecular properties (Appendix I, Section I.2), it is not the case for diffusion coefficients in solids, where the phenomena appearing are more complex. In solids, the molecule may be forced to follow a longer and tortuous path due to the blocking of the cross-sectional area, and thus the diffusion is somehow impaired. Several models have been developed to take into consideration this effect in the estimation of diffusion coefficients, leading, however, to a variety of results.

–4– Adsorption and Ion Exchange

4.1 BASIC PRINCIPLES OF ADSORPTION AND ION EXCHANGE 4.1.1 Adsorption materials Activated carbon, silica gel, and alumina are the most popular adsorbent materials in industry due to the fact that they provide large surface areas per unit weight. Activated carbon is produced from coconut shell, wood, and bone, whereas silica gel is made of hydrated silicon dioxide. Alumina can be either mined or produced by precipitated aluminum oxide and hydroxide. In the following sections, the most important adsorbents are presented in detail. Activated carbon Activated carbon can be manufactured from carbonaceous material, including coal such as lignite, peat, and wood, or nutshells such as coconut. The manufacturing process consists of two phases, carbonization and activation. The carbonization process includes drying and heating, the aim being the removal of several undesirable by-products from the raw material such as tar and other hydrocarbons. During the carbonization process, the material is heated at 400–600 °C in an oxygen-deficient atmosphere that cannot support combustion. Activated carbon has a very large surface area per unit weight, which makes it an ideal absorptive material. Generally, the total surface area of activated carbon ranges between 300 and 1500 m2/g, and the pore volume ranges between 0.7–1.8 cm3/g. Activated carbons made from petroleum coke can have a surface area of almost 3000 m2/g (Knaebel, 1995). Powdered activated carbon (PAC): PAC is made up of crushed or ground carbon particles, 95–100% of which will pass through a designated sieve—0.297 mm according to the American Water Works Association Standard, or 0.177 mm according to ASTM D5158. PAC is mainly used for biological treatment. Granular activated carbon (GAC): GAC is designated by mesh sizes such as 8 ⫻ 20, 20 ⫻ 40, or 8 ⫻ 30 for liquid-phase applications and 4 ⫻ 6, 4 ⫻ 8, or 4 ⫻ 10 for vapor-phase applications. GAC is used for the removal of toxic organic compounds from groundwater and industrial wastewater. Bituminous GAC: Bituminous coal has a more fully developed pore distribution, including “transport pores,” and is particularly effective for water treatment. It exhibits a relatively 243

244


large surface area (~900 m2/g) and an apparent density of approximately 0.50 g/cm3. With the exception of coconuts, this type of carbon is harder than other types, thus being more resistant to abrasion, and can be more vigorously backwashed without damage. Bituminous GAC is one of the most frequently used carbons for the treatment of low concentrations of low molecular weight organic compounds in water. Lignite GAC: This presents a total surface area of 650 m2/g and an apparent density of 0.50 g/cm3, approximately. It is usually used for liquid-phase adsorption, and specifically, in decolorizing applications because it has a higher percentage of meso (transitional) and macro pores than bituminous GAC, and therefore is appropriate for larger molecules. Coconut-shell-based GACs: These have a high portion of micropores and present surface areas generally over 1000 m2/g and apparent densities of about 0.50 g/cm3. Being manufactured mainly from vegetative material, they do not exhibit the fully developed pore structure of coal-based carbons. They are used in both vapor- and liquid-phase applications. Coconut shell–based carbon is slightly more expensive to produce than coal-based GAC, since about only 2% of the raw material is recoverable as GAC, versus 8–9% for coal-based carbons. In Table 4.1, the basic properties of common materials used in the manufacture of activated carbon are presented. A look into activated carbon Activated carbon is an adsorbent extensively used for the purification of water and gaseous waste streams. In relation to water treatment, it is generally effective in removing large organic molecules and nonpolar compounds from water, and its use is suggested for the following compounds (EPA, 2000): (a)

Organic compounds: aromatic solvents (benzene, toluene, nitrobenzenes, and xylene), chlorinated aromatics (PCBs, chlorobenzenes, chloronaphthalene, endrin, and toxaphene), phenols and chlorophenols (cresol, resorcinol, and nitrophenols), polynuclear aromatics (acenaphthene, benzopyrenes, naphthalene, and biphenyl), pesticides and herbicides (DDT, aldrin, chlordane, BHCs, heptachlor, carbofuran, atrazine, simazine, alachlor, and aldicarb), chlorinated Table 4.1 Basic properties of common materials used in the manufacture of activated carbon (Dabrowski et al., 2005; Streat et al., 1995)

Raw material

Carbon (%wt)

Volatiles (%wt)

Density (g/cm3)

Ash (%wt)

Wood Lignin Nutshells Lignite Coal Petroleum coke

40–45 35–40 40–45 55–70 65–95 70–85

55–60 58–60 55–60 25–40 5–30 15–20

1.25–2.5 2.5–3.33 0.71 0.74–1 0.55–0.8 0.74

0.3–1.1 – – 5–6 2–15 0.5–0.7

4.1 Basic Principles of Adsorption and Ion Exchange

(b)

245

nonaromatics (carbon tetrachloride, chloroalcyl ethers, and hexachlorobutadiene), high molecular weight hydrocarbons (dyes, gasoline, amines, humics, and kerosene, oil), and surfactants (alkyl benzene sulfonates). Inorganic compounds: chlorine, bromine, iodine, fluoride, antimony, arsenic, bismuth, chromium, tin, solver, mercury, cobalt, and zirconium.

In contrast, organic compounds like alcohols, low-molecular-weight ketones, acids and aldehydes, sugars and starches, very high molecular weight or colloidal organics, lowmolecular-weight aliphatics, and inorganic compounds such as nitrate, phosphate, chloride, bromide, iodide, lead, nickel, titanium, vanadium, iron, copper, cadmium, zinc, barium, selenium, molybdenum, manganese, tungsten, and radium are poorly absorbed on activated carbons. There are a number of cases where carbon adsorption is preferably used in water treatment: • • • • •

if compounds are not compliant for biodegradation, if the molecule contains branched chairs, has a large molecular weight, and low polarity, in the case of removing solids for concentrations lower than 50 ppm, in the case of removing oil for concentrations is lower than 10 ppm, generally, in the case of removing organics and inorganic species, for concentrations lower than 5000 and 1000 mg/L, respectively.

Besides the applications of water treatment, activated carbon is called into action for the removal of volatile organic compounds such as solvents, toxic gases, and odors from gaseous waste streams. Hydrogen sulfide, mercury, and radon can also be removed from gas streams by means of chemically impregnated carbons. In fact, among the adsorbents, it is the most attractive material because it is the only common nonpolar adsorbent (Noble and Terry, 2004). The polarity of the adsorbent’s surface plays a major role in the type of the compounds that can be adsorbed. Polar adsorbents will preferentially adsorb moisture in gaseous waste streams, and as a result their usage in such applications is limited. In contrast, activated carbon is an appropriate material in such cases, where the goal is the control of emissions of organic solvents, odors, toxic gases, acid vapors, phenols, and hydrocarbon vapors. Some basic rules for carbon adsorption are the following: • • • • •

Larger molecules adsorb better than smaller molecules. Nonpolar molecules adsorb better than polar molecules. Nonsoluble or slightly soluble molecules adsorb better than highly soluble molecules. On the basis of the polarity or solubility, or both, of the molecule being adsorbed, the pH may have an influence on the extent of adsorption. Temperature increases the rate of diffusion through the liquid to the adsorption sites; but since the adsorption process is exothermic, increases in temperature may reduce the degree of adsorption. This temperature effect is negligible in water-treatment applications and ambient vapor-phase applications.

246


The following are examples (see Table 4.2): • • • • • •

Alcohols are poorly adsorbed; they are very soluble and highly polar. Aldehydes are highly polar, and as the molecular weight increases, the polarity decreases and adsorbability increases. Amines are similar in structure to ammonia (NH3) except that the nitrogen is bonded to an organic group. Adsorption is limited by polarity and solubility. Chlorinated armoatics and chlorinated aliphatics are low-polarity and low-solubility compounds, which make them generally quite adsorbable. Glycols are water-soluble, and are not very adsorbable. Higher molecular weight organic compounds will generally be more adsorbable owing to adsorptive attraction relative to size.

Largely, the same principles apply for water treatment. Consequently, activated carbon is suitable for organic molecules that are nonpolar and of high molecular weight. Trichloroethylene, benzene, ethylbenzene, toluene, and xylene are easily adsorbed in the gas phase when activated carbon, for instance, is used. On the other hand, adsorption is not preferably selected in applications in relation to aldehydes, ketones, and alcohols. In a successful application, reduction in emissions from 400–2000 ppm to under 50 ppm can be achieved (EPA, 1999), especially for VOCs with boiling points between 20 –and 175 °C. Table 4.2 From stronger adsorption to weaker adsorption on activated carbon Compound

Molecular weight

Boiling point (°C)

Nitrobenzene Tetrachloroethane Tetrachloroethylene Styrene Xylene Naphthalene Toluene Benzene

123 166 165 104 106 128 92 78

MTBE Hexane Ethylacrylate Dichloroethane Methylethylketone Methylene chloride Acrilonitrile

88 86 100 99 72 84 53

Acetone Vinylchloride Chloroethane Bromotrifluoromethane Methane

58 62 64 149 16

211 147 121 145 138 217 111 80 ⬎10% of carbon capacity 55 68 57 99 80 40 74 ⬍ 1% of carbon capacity 56 ⫺14 12 ⫺58 ⫺161


247

For optimum efficiency, humidity levels, temperature, and pressure should be monitored and controlled during the adsorption. The adsorption process of VOCs removal is exothermic in the most cases, which should be considered as a significant design parameter, since there is a risk of fire in the removal of high loads of organic compounds that exhibit high heats of adsorption. Activated alumina Activated alumina is amorphous or crystalline alumina, which has been partially or completly dehydrated and has a large surface area per unit mass. Activated alumina is made from hydrated alumina, namely, Al2O3.nH2O, where n ⫽1~3, by calcining to get n closeto 0.5 (Knaebel, 1995). It is a white or tan-colored material of chalky appearance. Stable crystalline forms are usually not thought of as adsorbents because they have very low surface areas (Knaebel, 1995). However, transitional forms, such as gamma and beta alumina (Figure 4.1), have high surface areas, typically between 200 and 400 m2/g. They contain pores with diameters largely in the range 2–5 nm (Noble and Terry, 2004). Being extremely hydrophilic materials, they find application mainly in the removal of water from acid, gas, or organic solvent streams. Silica gel Silica gel is synthetic amorphous silica consisting of a compact network of spherical colloidal silica particles. Its surface area is typically between 300 and 850 m2/g. The predominant pore diameters are in the range 22–150 Å. Silica gel is produced via the following procedure: a sodium silicate solution reacts with a mineral acid, such as sulfuric acid, producing a concentrated dispersion of finely divided particles of hydrated SiO2,

Figure 4.1

The structure of beta alumina.

248


which in turn polymerizes to form the silica gel. The treatment of silica gel involves also washing, drying, and activation. Silica gel is mainly used for water removal (Noble and Terry, 2004). Organically modified clays Organically modified clays are mixtures of anthracite and bentonite clay or bulk clay. The surface of the clay is usually activated after treating with a quaternary amine. These clays are employed only in the purification of gas streams. Polymeric resin adsorbents Polymeric resins found in liquid treatment can be classified into three main categories: (a) (b) (c)

the carbonized ion-exchange resins, the divinylbenzene (DB) adsorbents, and the post-cross-linked adsorbents.

Each type is produced by following a different procedure and has its own advantages and limitations. Carbonized resins have the advantage of being structured with the desired pore distribution depending on the contaminant to be adsorbed. Divinylbenzene adsorbents can be used in environments with high relative humidity, since they are very hydrophobic materials. In general, the most important feature of polymeric resins is that they adsorb the pollutant without reacting with them. For example, the adsorption on activated carbon is generally an exothermic process that may lead to temperature elevation and bed-fire outbursts. On the other hand, safer and controlled operation can be assured using polymeric adsorbents, since they are much less reactive materials. Zeolite molecular sieves Zeolite molecular sieves are composed of silicon and aluminum and can be natural or manmade minerals. Molecular sieves are crystalline, hydrated aluminosilicates of (most commonly) sodium, calcium, potassium, and magnesium. The alumininosilicate portion of the structure is a three-dimensional open framework consisting of a network of AlO4 and SiO4 tetrahedra linked to each other by sharing all of the oxygens (Sherman, 1978). Zeolites may be represented by the empirical formula R 2/n n⫹⭈Al 2 O3 ⭈X SiO2 ⭈Y H 2 O where X is generally equal to or greater than 2, and n is the cation valence (Sherman, 1978). The zeolite framework contains pore channels and interconnected voids and cavities, which are occupied by cations and water molecules. These cations are generally mobile and can be exchanged. A typical synthetic zeolite is the zeolite ZSM-5 shown in Figure 4.2. This zeolite is extensively used as a catalyst. In Table 4.3, the basic characteristics of the most important zeolite molecular sieve species are presented. Zeolite micropore openings are of the same order of magnitude as


Figure 4.2

249

The structure of ZSM-5 zeolite. Table 4.3

Basic characteristics of some important zeolite molecular sieve species (Sherman, 1978) Zeolite type

Pore openings (hydrated form)

Chabazite Clinoptilolite

3.7 ⫻ 4.2 and 2.6 Å 4 ⫻ 5.5, 4.4 ⫻ 7.2, and 4.1 ⫻ 4.7 Å 6.7 ⫻ 7 and 2.9 ⫻ 5.7 Å 4.2 ⫻ 4.4, 2.8 ⫻ 4.8, and 3.3 Å 4.2 and 2.2 Å 3.7 Å 7.4 and 2.2 Å 7.4 and 2.2 Å

Mordenite Philipsite LINDE A LINDE F LINDE X LINDE Y

Typical SiO2/Al2O3 mole ratio 4 10 10 4.4 2 2 2.5 2.8

the sizes of numerous molecules such as helium, water, hydrogen, nitrogen, oxygen, carbon monoxide and dioxide, and sulphur dioxide, which are between 2.2 and 4.1 Å, and methane, ethane, propane, benzene, cyclohexane, and chloroform, which are between 3.8 and 6.1 Å (Knaebel, 1995). Zeolites are suitable for heavy-duty purifications of gas streams. Moreover, like resins, they can be manufactured with pore structures of the desired diameter in order to be used for the selective adsorption of specific contaminants based on their molecular size. They are resistant to temperature rise, being stable at temperatures up to 800 °C in dry air and up to 500 °C in humid air. Therefore, they can be treated at high temperature with air to avoid the

250


Figure 4.3 The structure of zeolite Y.

formation of contaminated condensate. They also assure safe operation since they are much less reactive than activated carbon when adsorbing reactive organic substances. Due to the larger size of sorbates and the lower diffusion rates in liquids, larger pore sizes are needed to treat liquid effluents, normally in the range 30 Å (Noble and Terry, 2004). For gas-phase effluents, the pores sizes are in the range 10 to 25 Å. For example, zeolite Y (Figure 4.3) exhibits the FAU (faujasite) structure. It has a three-dimensional pore structure with pore diameter 7.4 Å and cavity of diameter 12 Å. Low-cost adsorbents for wastewater treatment Some low-cost adsorbents for the treatment of wastewater, especially for the removal of heavy metals, are the following (Babel and Kurniawan, 2003): •

•

Chitosan: Chitosan has a molecular structure similar to cellulose. This material is produced from chitin, which is widely found in the exoskeleton of shellfish and crustaceans. Chitin is the second most abundant natural biopolymer after cellulose. Chitosan is a good adsorbent for all heavy metals. It has been estimated that chitosan can be produced from shellfish and crustaceans at a market price of 15.43 $/kg. Peat moss: Peat moss is a complex soil material containing lignin and cellulose as major constituents. It is a widely available natural substance. It has a large surface area


• •

251

(⬎200 m2/g) and is highly porous. Peat moss is a relatively inexpensive material and is commercially sold at a price of 0.023 $/kg in the united States. Fly ash: Fly ash is an industrial solid waste generated in thermal power plants. Fly ash can be easily solidified after the heavy metals have been adsorbed. Iron (III) hydroxide waste: Especially for heavy metals removal from wastewater, iron (III) hydroxide waste and waste slurry from the fertilizer industry, xanthate, rice husk, carbon, and coconut shell have been studied and can be considered as alternatives.

In Tables 4.4–4.7, several important adsorbents data are tabulated. Safety considerations Adsorption is generally an exothermic process leading to temperature rise. Although this property is useful when storage of heat is desired, it is not the case in the adsorption of VOCs. If certain hydrocarbons are involved, the carbon or metals on the carbon may catalyze the oxidation of these pollutants when the adsorbent is hot, resulting in bed fires that deteriorate the adsorbent by either altering the its pore size or converting part of it to ash. Cooling of the bed or humidification of the air can be employed to avoid the outburst of Table 4.4 Basic characteristics of common adsorbents (Perry and Green, 1999) Adsorbent

Hydrophilic

Activated carbon Polymers Silica gel Activated alumina Carbon molecular sieves Silicalite Zeolites (3A, 4A, 5A, 13X)

Hydrophobic

Amorphous

Structured

Table 4.5 Physical properties of common adsorbents (Perry and Green, 1999; Knaebel, 1995) Adsorbent

Micropore size (Å)

Internal porosity p (%)

Bulk density b (kg/m3)

Surface area S (m2/g)

Adsorption Capacity Qmax (g/g)

Carbons Organic polymers Silica gel Aluminas Zeolites (3A, 4A, 5A, 13X)

⬎ 6a Varying ⬎10 ⬎6b 3.9–8

35–85 40–60 38–48 30–57 30–38

250–700 420–700 700–820 700–930 580–680

100–1800 80–700 600–800 200 600–700

0.3–0.7 0.45–0.55 0.35–0.50 0.1–0.33 0.21–0.36

a b

Activated carbon. Activated alumina.

252


Table 4.6 Alternative adsorption media (Noble and Terry, 2004)

Activated carbon Modified clays Polymeric resins Zeolites

Typical adsorbates

Cost of adsorber (1998) ($/lb)

Nearly all organics Emulsified oil and grease Chlorinated and nonchlorinated VOCs Chlorinated and nonchlorinated VOCs

1 1.5 8–35 7–10

Table 4.7 Adsorbent types (Noble and Terry, 2004) Adsorbent

Advantages

Disadvantages

Activated carbon

Cheapest hydrophobic adsorbent Higher capacity than other materials (e.g. zeolites)

Difficult regeneration, fire risk during regeneration Not so effective in removing traces of water from gases

Silica gel, activated alumina

Zeolites Silicalite Polymer adsorbents (usually styrene) Biosorbents (activated sludge on a porous support)

Separations can be based both on polarity and geometry Can be burned off more easily than activated carbon No fouling problems compared to activated carbon No regeneration needed

Applications

Removal of organics from aqueous and gaseous streams Drying of gas streams (both), hydrocarbon removal from gases (silica gel) Lower capacity than Dehydration, many other adsorbents air separation

More expensive than activated carbon Much more expensive than activated carbon Lower effective capacity than other adsorbents

Removal of organics from gaseous streams Removal of organics from gaseous streams Removal of organics from gaseous streams

such fires. However, such effects are not only connected to the VOC to be removed but also to the type of the adsorbent. Specifically, polymers are usually used at mild temperatures, which do not allow bed fires. On the other hand, although zeolites are not flammable, they can contribute to intense heat release by promoting oxidation reactions after a minimum temperature has been provided for the start of the catalytic behavior. Normally, in case of a bed fire, the pore size of the zeolite is not altered by temperature (up to a point) since zeolites are resistant to temperature rise as mentioned above. However, the pore size of the zeolite may be decreased leading to an increase in its density, if the temperature surpasses the meta-stable range.


253

4.1.2 Ion-exchange materials According to the definition given by Helfferich (1962), ion exchangers are insoluble solid materials, which carry exchangeable cations or anions. When the ion exchanger is in contact with an electrolyte solution, these ions are exchanged with an equivalent amount of other ions of the same sign. Cation and anion exchangers are the materials that carry cations and anions, respectively. Some materials have the ability of both cation and anion exchange and are called “amphoteric ion exchangers” (Helfferich, 1962). There are a number of different natural and synthetic materials that show ion-exchange properties. The most important materials are described in the following sections. Naturally occurring ion exchangers Natural inorganic ion exchangers Many natural minerals, such as clays (e.g. bentonite) and zeolites (e.g. clinoptilolite), are capable of exchanging ions. However, crystalline aluminosilicates are principally the common natural minerals with cation-exchange properties (Hellferich, 1995). Amongst natural inorganic ion exchangers, zeolites and clays are the most common materials. Other natural aluminosilicate materials, such as green sand, are also found in specific waste-treatment applications. Zeolites have an open, three-dimensional framework structure with pores (channels) and interconnecting cavities in the alumosilicate lattice. In Table 4.8, the basic characteristics of the most important zeolite species of commercial use are presented. Clays have a loose layer structure (Figure 4.6). Characteristic minerals are montmorillonite and beidellite. Aluminosilicates such as montmorillonite, kaolinite, and feldspar can act as cation and anion exchangers. Natural inorganic ion exchangers have the following disadvantages: • • • • • • •

low exchange capacity low mechanical durability low abrasion resistance pore size cannot be adjusted clay minerals tend to convert to a colloidal form difficult to size mechanically may be partially decomposed into acids or alkalis. Table 4.8 Basic characteristics of some important zeolites (Hanson, 1995)

Species

Formula

Internal porosity (%)

Density (g/cm3)

Ion-exchange capacity (meq/g)

Phillipsite Chabazite Mordenitea Clinoptiloliteb

(K,Na)10[(AlO2)(SiO2)22]·20H2O Ca2[(AlO2)4(SiO2O8)]·13H2O Na8[(AlO2)8(SiO2)40]·24H2O Na6[(AlO2)6(SiO2)30]·24H2O

31 47 28 34

2.15–2.20 2.05–2.10 2.12–2.15 2.16

3.87 3.81 2.29 2.54

a b

Figure 4.4. Figure 4.5.

254


Figure 4.4

Figure 4.5

The structure of mordenite.

The structure of clinoptilolite.


Figure 4.6

Natural organic ion exchangers • • •

255

The typical structure of clays.

Some common natural organic ion exchangers are

polysaccharides, such as cellulose and peat proteins, such as casein, keratin, and collagen carbonaceous materials, such as charcoals, lignites, and coals.

However, only charcoals, coal, lignite, and peat are used in industrial applications. Although they are not as effective as synthetic organic exchangers, their low cost makes them an appealing choice. Before selecting these materials, one should keep in mind that • • • • • •

they exhibit low exchange capacities they tend to swell or convert into a colloidal form they have a loose physical structure their physical properties are not uniform they are nonselective they are pH-sensitive

Synthetic ion exchangers Synthetic inorganic ion exchangers Zeolites: Synthetic zeolites (Figure 4.7) are available as powder, pellet, or bead. The greatest advantage of synthetic zeolites against natural zeolites is that they can be manufactured with a wide variety of physicochemical properties. The main disadvantages of synthetic zeolites are • • •

their high cost compared to natural zeolites they are pH-sensitive their limited mechanical stability

Titanates and silico-titanates: The oxide and hydroxide of titanium are effectively used in applications of removing metal ions from water. Early studies (since 1955) have shown that hydrous titanium oxide is the most appropriate material for extracting uranium from seawater, whereas titanates and hydrous titanium oxide are suitable for removing strontium.

256


Figure 4.7 Simplified three-dimensional representation of a zeolite structure. Each Si or Al atom is surrounded by four oxygen atoms, forming tetrahedron elementary structures (primary building units).

These materials have been used for in-tank precipitation at the Savannah River Site in the United States (IAEA, 2002). Transition metal hexacyanoferrates: These metals are very effective in extracting radioactive cesium from solutions. In India, potassium cobalt(II) hexacyanoferrate(II) has been prepared in a column-usable granular form that needs no resin support (IAEA, 2002). Synthetic organic ion exchangers The largest group of ion exchangers available today is synthetic organic resins (Tables 4.14 and 4.15). The resin matrix is a flexible network of hydrocarbon chains, where fixed ionic charges at various fixed positions are contained. The resins are made insoluble by cross-linking the various hydrocarbon chains. The degree of cross-linking determines many of the resin properties. Specifically, if resistance to mechanical degradation is desired, a high degree of cross-linking leads to a hard resin that is not very porous and not susceptible to swelling. Here, let us recall that swelling takes place when an organic ion exchanger is placed in a solvent or solution. Besides the exchanger itself, the properties of the solution or solvent determine the extent of swelling. The main advantages of synthetic organic ion-exchange resins are • • •

high capacity wide versatility low cost compared to some synthetic inorganic media.


257

However, there also some disadvantages. Specifically, the limited radiation and thermal stability set limits to the usage of synthetic organic ion-exchange resins. Regarding temperature, 150 °C is the maximum temperature that cation-exchange resins can withstand, whereas 70 °C is the limit for anion-exchange resins. Consequently, hot streams to be treated have to be cooled below these temperatures. In Tables 4.9 and 4.10, a comparison of organic and inorganic ion exchangers is presented. Classification based on matrix Polystyrene divinylbenzene: Ion-exchange resins are commonly manufactured from a copolymer of styrene (Figure 4.8) and divinylbenzene (Figure 4.9). The divinylbenzene content in the matrix determines the degree of cross-linking. So, 5% mol divinylbenzene

Table 4.9 Qualitative comparison of organic and inorganic ion exchangers Property

Organic exchangers

Inorganic exchangers

Chemical stability Thermal stability Mechanical strength Exchange capacity Regeneration

Good Fair to poor Good High Good

Immobilization

Good; imobilized in a variety of matrixes or can be incinerated Medium to high

Fair to good Good Variable Variable Limited regeneration performance Good; converted into equivalent mineral structures Low to high

Cost

Table 4.10 Quantitative comparison of organic and inorganic cation exchangers Exhanger

Capacity (kg/ft3)

Natural zeolites Synthetic zeolites Organic Sulfonated coal (carbonaceous) Organic Synthetic Phenolic Organic Synthetic Styrene base Organic synthetic resins

3–5 12–16 5–7 6–18 20–30 10–22

258


Figure 4.8

Figure 4.9

Cross-linked polystyrene.

Divinilbenzene (DVB).

in the matrix corresponds to 5% cross-linking. As mentioned above, the degree of crosslinking is connected to the properties of the resin. Low divinylbenzene content means low cross-linking and the result is a soft resin prone to swelling in solvents. Then, the ionexchange potential is created by introducing fixed ionic groups into the resin matrix. For example, in sulfonation, 8–10 –SO3H groups are introduced in the matrix for every 10 benzene rings. The H⫹ ions contained in the –SO3H groups is the mobile ion or counterion that will be exchanged in a solution with different cations. Phenolic: There are various kinds of phenolic resins. The ones that are produced from phenol–formaldehyde condensation are very weak acid exchangers, where the phenolic –OH groups are the fixed-ionic groups. The formaldehyde content decides the extent of cross-linking in the resin. On the other hand, phenolsulfonic acid resins contain both strong acid –SO3H and weak acid –OH groups. Acrylic: This resin is manufactured from a copolymer of acrylic or methacrylic acid with divinylbenzene (Figure 4.10). The result is a weak acid ion-exchange resin with –COOH groups that have very little salt splitting capacity, but are very effective in removing Ca2⫹ and similar ions under alkaline conditions. Classification based on the functional groups On the basis of the charge of the exchangeable ions, there are cation (positive mobile ions) and anion (negative mobile ions) resins. Both types are manufactured from the very same basic organic polymers. However, the ionic groups that are introduced into the matrix


Figure 4.10

259

Polyacrilic matrix (cross-linked polymethacrylate).

Table 4.11 Functional groups of standard ion-exchange resins (IAEA, 2002; Helffercih, 1962) Type

Functional group

Strongly acidic (cation exchangers) Weakly acidic (cation exchangers) Other acidic (cation exchangers)

⫺SO3– ⫺COO– ⫺PO32– ⫺HPO2– ⫺AsO32– ⫺SeO3– ⫺[N(CH3)3]⫹ ⫺[N(CH3)2C2H4OH]⫹ ⫺NH3⫹ R1–NH2⫹⫺R2

Strongly basic (anion exchangers) Weakly basic (anion exchangers)

define the chemical behavior and the specific applications of the resin. Generally, resins can be typified into strong or weak acid cation exchangers and strong or weak base anion exchangers (Table 4.11). Strong acid cation resins: These resins are highly ionized in both the acid (R⫺SO3H) and salt (R⫺SO3Na) form and behave like strong acids. They can convert a metal salt into the corresponding acid by the reaction 2(R ⫺ SO3 H)⫹ NiCl 2 (R ⫺ SO 4 ), Ni ⫹ 2 HCl Na⫹ and H⫹ ions are highly exchangeable over the entire pH range. Depending on the specific usage, these resins are used either in the hydrogen or in the sodium form. For example, the sodium form is preferred if water softening (calcium and magnesium removal) is the desired action (Table 4.12). The resins can be regenerated by treatment with the appropriate solution (a strong acid for the hydrogen form or a sodium chloride solution for the sodium form). The treatment with hydrochloric acid (HCl) results in a concentrated nickel chloride (NiCl) solution according to the previous chemical equation. Weak acid cation resins: Here, the ionic group is a carboxylic acid (⫺COOH) and these resins behave as if they were weak organic acids. They have a much higher affinity than strong acid resins for hydrogen ions. Therefore, fewer amounts of acids are required in

260


Table 4.12 Selectivity of ion-exchange resins in decreasing order of preference Strong acid cation exchanger

Strong base anion exchanger

Barium Lead Calcium Nickel Chloride Cadmium Copper Zinc Magnesium Potassium Ammonia sodium Hydrogen

Iodide Nitrate Bisulfite Cyanide Bicarbonate Hydroxide Fluoride Sulfate

their regeneration compared to strong acid resins. The dissociation of a weak acid resin and thus its capacity is greatly affected by the pH of the solution. Strong base anion resins: These resins are used in the hydroxide (⫺OH) form for water deionization and their capacity is not influenced by pH. So, they can be used over the entire pH range. They can convert an acid solution into pure water: R ⫺ NH 3 OH ⫹ HCl R ⫺ NH 3 Cl ⫹ H ⫺ OH It is easily understood that their regeneration is achieved by treating them with concentrated sodium hydroxide (NaOH). Weak base anion resins: Just like for weak acid resins, the operation of weak base anion resins is greatly affected by pH. They exhibit their maximum exchange capacity in the pH range up to 7.0. They hardly adsorb any strong acids: they cannot split salts. Heavy-metal-selective chelating resins: The behavior of chelating resins resembles that of weak acid cation resins. They differ in that they are much more selective in heavy metal cations. Chelating resins tend to form stable complexes with the heavy metals and contain EDTA as the functional group. In the sodium form, they are referred to as EDTA-Na. Their groups have donor atoms such as sulfur and nitrogen and may develop bonds with metals that exhibit both covalent and ionic characteristics. Chelating resins have been extensively studied and their sorption properties are well known. Besides their selectivity in removing transition metals, another feature in favor of their use is that they can be easily regenerated with mineral acids. There are also resins that do not contain negative ionic groups but are also capable of forming complexes with metals. For example, picolylamine resins create stable complexes at low pH values. These kinds of complex-forming resins are suitable for removing trace amounts of metal cations from solutions containing high amounts of competing alkaline and alkaline-earth metal ions at acidic pH. In Table 4.13, the relative selectivity of a commercial chelating resin for heavy metal cations is shown. The relative selectivity is based on the selectivity for calcium ions. For


261

Table 4.13 Chelating cation resin relative selectivities for metal ions (relative selectivity is based on Ca2⫹) Metal ion

Relative selectivity

Hg2⫹ Cu2⫹ Pb2⫹ Ni2⫹ Zn2⫹ Cd2⫹ Co2⫹ Fe2⫹ Mn2⫹ Ca2⫹

2800 2300 1200 57 17 15 6.7 4.7 1.2 1

example, the preference for mercury is 2800 times that for calcium. This means that in the treatment of a solution, which contains equal molar concentrations of mercury and calcium ions, the molar concentration of mercury ions on the resin will be 2800 times that of calcium ions. In Figure 4.11, the formula of a chelating resin is shown with the iminodiacetic acid group as the chelating ligand, which is bonded onto a cross-linked polystyrene matrix. This type of resin shows a large affinity for alkaline-earth and transition metal ions. In Figure 4.12, three different amines that are used as functional groups for the manufacture of chelating resins are shown. Composite ion exchangers If it is considered necessary for an ion exchanger to present a certain number of characteristics that cannot be found in only one adsorbent, then a composite ion exchanger can be chosen. They consist of one or more ion exchangers combined with another material, which can be inorganic or organic. For example, if it is desired that the exchanger be

Figure 4.11 Chelating resin with the iminodiacetic acid group as the chelating ligand bonded onto a cross-linked polystyrene matrix.

262


Figure 4.12

Amine groups for the manufacture of chelating resins.

removed from the liquid by means of a magnetic field, an inorganic exchanger containing magnetite can be used (IAEA, 2002). Another reason in favor of composite ion exchangers is that in this way, a granular material appropriate for column use may be formed from ion exchangers that normally do not form or only form weak granules themselves, as long as a proper inorganic binder is used. Chemical stability of resins The chemical stability of resins may be degraded due to chemical interactions with oxidizing agents. The latter interact with CH2 groups and gradually lead to the destruction of the matrix. The main oxidizing substances are • • • •

O2 at high temperatures H2O2 leads (Fenton reaction) to the complete dissolution/destruction O3 splits aromatic double bonds Cl2 decomposes cation exchangers, and oxidizes amino and ammonium groups of anion exchangers • CrO3 (chromic acid) destroys the cross-linking of cation exchangers • Na2S2O8 separates functional groups of anion exchangers • HNO3 may damage polycondensation cation exchangers, easily damages functional groups of anion exchangers. However, reductive agents may also affect resins. For example, hydrogen may damage sulfonic acid groups of strongly acidic cation exchangers. 4.1.3 Kinetics Ion exchange shares many characteristics with adsorption, such as mass transfer from the fluid to the solid phase; there are, however, some significant differences. Specifically, although both processes can be characterized as sorption processes, the sorbed species are ions in ion exchange, whereas electrically neutral substances are sorbed in adsorption. Moreover, in ion exchange, the ions removed from the liquid phase are replaced by ions from the solid phase. So, there actually occurs an exchange of ions and not only a removal


263

Table 4.14 Physical properties of ion-exchange resins (Perry and Green, 1999) Resin type

Bulk wet density b (kg/m3)

Maximum operating Exchange capacity temperature (dry resin) Tmax (°C) Qmax (meq/g)

Strongly acidic cation exchangers Weakly acidic cation exchangers Strongly basic anion exchangers Weakly basic anion exchangers

740–900 670–800 670–700 610–720

50–150 45–120 40–100 60–100

2–5.5 0.7–10 0.57–3.8 0.9–5.5

Table 4.15 Capacity of most common ion-exchange resins (Helfferich, 1995) Matrix

Ionic group

Trade name

Polystyrene resins

⫺SO3–

Amberlite IR-120, 122, 124 Dowex 50 Duolite C-20, 25, 27 Lewatit

S-100, 115 C-63 IRA-400

4.6–4.75 ⫺N(CH2COOH)2 ⫺PO32– 6.6 ⫺N(alkyl)3⫹ 2.6

Dowex A-1 Duolite

Exchange capacity (dry resin) (meq/g) 4.3–5 4.9–5.2 5–5.1

1–1.2

Amberlite Amberlite

IRA-401, 402, 405

3 Dowex-1, 11 Lewatit

M-600, MP-600 Phenolic resins C-3, 10 ES-65 Vinyl addition polymers IRC-50

3.7 ⫺SO3– 2.9 ⫺PO32– 3.3 ⫺COOH 9.5

Condensation polymers

⫺N(alkyl)3⫹

3.5

Duolite Duolite Amberlite Zeo-Karb 226 Lewatit MN

10 2.3

in the latter process. In addition, the total charge sorbed and desorbed is exactly the same as imposed by the electroneutrality principle (Noble and Terry, 2004; Perry and Green, 1999). As Helfferich (1962) states: “Ion exchange is inherently a stoichiometric process.” Apparent deviations from this behavior can occur because of electrolyte sorption and desortpion during ion exchange (Helfferich, 1962).

264


Furthermore, in ion exchange, the fluxes of at least two different ions are coupled with each other and this process cannot be described by the use of a simple diffusion equation, as in the case of adsorption and the exchange of isotopes, i.e. isotopic exchange. This electric coupling of the ionic fluxes and the stoichiometric nature of the ion-exchange process are the most important features, which distinguish ion exchange from adsorption and isotopic exchange (Helfferich, 1962). As a result, the quantitative treatment of ion exchange is much more complicated than adsorption or isotopic exchange. It is generally accepted that adsorption and ion exchange can be grouped together as sorption for a unified treatment in practical applications. Most mathematical theories and approaches have been developed originally for adsorption rather than ion exchange. However, especially in the case of fixed beds, they are sufficiently general to be applicable with minor, if any, modifications to a number of similar phenomena such as ion exclusion and ligand exchange. According to Helfferich, the applicability of a simplified theory hinges on the mode of operation rather than on the particular mechanism of solute uptake (Helfferich, 1962). In the present book, the analysis of the ion exchange and adsorption kinetics is based on a simplified unified approach and only some solutions are given, especially for ion exchange. On the other hand, the models for adsorption and isotopic exchange are equivalent. A significant feature of adsorption is that the rate of physical adsorption is generally too high and consequently, the overall rate is controlled by mass transfer (or heat transfer) resistance, rather than by the intrinsic sorption kinetics (Ruthven, 1984). Following this approach, adsorption is viewed and termed in the present book as a “diffusion-controlled” process. The same holds for ion exchange. As long as the rate of adsorption and ion exchange is determined by diffusion processes, the rate laws are derived by applying the well-known diffusion equations (Helfferich, 1962). In general, diffusion processes are described in terms of Fick’s first law: J i ⫽⫺D grad Ci

(4.1)

where Ji is the flux of the diffusing species i, Ci its concentration, and D the diffusion coefficient. The diffusion of a species in a particle is a special case: the solid matrix occupies a substantial fraction of the particle volume and obstructs diffusion. In addition, following a more rigorous approach, the solid matrix may consist of small microporous crystals formed into a macroporous structure. In this case, two distinct diffusional resistances exist: macropore and micropore resistance (Ruthven, 1984). In the related literature, macropore diffusion is also termed “pore diffusion” while microporous diffusion is termed “solid diffusion” (Perry and Green, 1999). The term “intraparticle diffusion” includes pore and solid diffusion mechanisms. Pore diffusion is the diffusion of solutes in fluid-filled pores. These pores are so large that the solute escapes the force field of the adsorbent surface (Perry and Green, 1999). On the other hand, solid diffusion is the diffusion of solutes in the adsorbent surface. In this case, the pores are so small that the solute never escapes the force field of the adsorbent surface. The transport of solute molecules occurs by an activated process involving jumps between adsorption sites (Perry and Green, 1999). Pore and solid diffusion act in parallel, and thus the dominant transport process is the faster one.


265

For the needs of the present book, in the analysis of adsorption and ion-exchange kinetics, Helfferich’s approach is followed—the particle, consisting of pore fluid and a solid matrix, is treated as a single quasi-homogeneous phase, regardless of its inhomogeneities in molecular or colloidal dimensions and its particular geometrical structure (Helfferich, 1962). The model solutions given by Helfferich are equivalent to the solutions of a solid diffusion–controlled process. Eq. (4.1) is valid and the corresponding diffusion coefficient is an “effective” kinetic parameter characterizing the ease of movement of the solutes in the particle in macropores and micropores. Furthermore, the diffusion coefficient is considered to be constant, or to be more realistic, an average value as it can be dependent on the solute solid-phase concentration. Following this simplified approach, the time dependence of the concentration is related to the flux by Fick’s second law (material balance or condition of continuity): Ci ⫽⫺div J i t

(4.2)

Combining eqs. (4.1) and (4.2), and for spherical particles, the following diffusion equation, written in spherical coordinates (r), describes the mass transfer process:  2C 2 Ci  Ci ⫽ D  2i ⫹ t r r   r

(4.3)

where D is the solid diffusion coefficient and Ci the solid-phase concentration of the solute (in the following chapters, this concentration is denoted by q). Eq. (4.3) must be solved under the appropriate initial and boundary conditions. Here, it should be noted that for most nonspherical particles, their representation as an equivalent sphere is an acceptable approximation, and thus the above-mentioned equation can be used in this case as well. Apart from the diffusion step in the particle, when the uptake process occurs from a binary or multicomponent fluid mixture, there maybe an additional resistance to mass transfer associated with the transport of solutes through the fluid layer surrounding the particle. The driving force in this case is the concentration difference across the boundary layer, and the flux at the particle surface is J i ⫽ kf (C i, f ⫺ C i,s )

(4.4)

where kf is the mass transfer coefficient and Ci,f and Ci,s are the concentration of solute in the bulk fluid and at the particle surface phase, respectively. Thus, the analysis of the rate-determining step, as analyzed for heterogeneous processes in Section 3.1.2, is equally applied in adsorption and ion exchange. The only difference is that the diffusion processes in the fluid film and in the particle are followed by physical adsorption or ion exchange and not by a reaction step as in catalysis. The analysis of adsorption and ion-exchange kinetics is presented in detail in Section 4.2.1, and is based on the diffusion processes and equations rather than on some kind of

266


adsorption or ion-exchange kinetics. However, the shrinking core model and some kinetictype models are presented in brief. 4.1.4 Equilibrium A common way to represent the equilibrium in adsorption and ion-exchange systems is the equilibrium isotherm. The equilibrium isotherm represents the distribution of the adsorbed material between the adsorbed phase and the solution phase at equilibrium. This isotherm is characteristic for a specific system at a particular temperature. The basic difference between adsorption and ion exchange is that while there is only one isotherm at a specified temperature for adsorption, more than one isotherm can exist at a specified temperature for different normalities of the solution in the exchange of ions of different valences due to the concentration–valence effect (Helfferich, 1962). Thus, a specific ion-exchange system presents one equilibrium curve (isotherm) only under constant temperature and normality. This is why, while the term “isotherms” is used for the equilibrium curves in the case of adsorption, the term “isotherm–isonormal” should be used for ion exchange. In the following sections, the most important isotherm types are presented. Adsorption Langmuir isotherm Adsorbents that exhibit the Langmuir isotherm behavior are supposed to contain fixed individual sites, each of which equally adsorbs only one molecule, forming thus a monolayer, namely, a layer with the thickness of a molecule (Perry and Green, 1999): qe KCe ⫽ QM 1⫹ KCe

(4.5)

where qe is the solid-phase concentration in equilibrium with the liquid-phase concentration Ce, QM is the final sorptive capacity (most commonly in mg/mg), and K is an equilibrium constant (most commonly in L/mg). The units of K are L/mol provided that Ce is expressed in (mol/L). Applying the same equation for Ce = Co, qmax KCo ⫽ 1⫹ KCo QM

(4.6)

where qmax is the solid-phase concentration in equilibrium with Co. Dividing the above equations: qe C 1⫹ KCo ⫽ e qmax Co 1⫹ KCe

(4.7)


267

Introducing the equilibrium parameter (La) as La ⫽

1 1⫹ KCo

(4.8)

the dimensionless form of Langmuir equation is Y⫽

X La ⫹ (1⫺ La ) X

(4.9)

qe qmax

(4.10)

Ce Co

(4.11)

where Y⫽

and X⫽

By using the dimensionless equilibrium relationship, La can be expressed as La ⫽

X (1⫺ Y ) Y (1⫺ X )

(4.12)

Langmuir adsorption isotherm constants for several compounds on several adsorbents/water systems at ambient temperature are presented in Table I.23, Appendix I. Freundlich Adsorbents that follow the Freundlich isotherm equation are assumed to have a heterogeneous surface consisting of sites with different adsorption potentials, and each type of site is assumed to adsorb molecules, as in the Langmuir equation (Perry and Green, 1999):

qe ⫽ kCeFr QM

(4.13)

where qe is the solid-phase concentration in equilibrium with the liquid-phase concentration Ce, QM is the final sorptive capacity, and k and Fr are equilibrium constants. Another form of the Freundlich isotherm is the following:

qe ⫽ K FCeFr

(4.14)

268


where K F ⫽ QM k

(4.15)

qmax ⫽ kCoFr (1) QM

(4.16)

Applying eq. (4.13) for Ce = Co,

Here, qmax is the solid-phase concentration in equilibrium with Co. Dividing eqs. (4.13) and (4.16), Y ⫽ X Fr

(4.17)

It is important to distinguish between the values of qmax and QM. The first is the solid-phase concentration in equilibrium with the initial fluid-phase concentration, while QM is higher, representing the maximum adsorption capacity, which is typically achieved in higher fluidphase concentrations. Following the terminology of Inglezakis (2005), for ion exchange qmax corresponds to the maximum exchange level (MEL), while QM corresponds to the real exchange capacity (REC). The empirical constants La and Fr are related to the particular system under investigation and are obtained from laboratory experiments (Chen et al., 1968; Chern and Chien, 2002). Generally, an isotherm is favorable if its shape is convex upward, and unfavorable

1

0.8

Y

0.6

0.4 Rectangular Sigmoidal

0.2

Favorable Linear Unfavorable

0 0

0.2

0.4

0.6 X

Figure 4.13

Basic isotherm types.

0.8

1


269

if its shape is concave upward in an X versus Y figure. In Figure 4.13, the characteristic isotherm shapes are presented. The parameter La is also called the “separation factor” and provides a quantitative description of the equilibrium regions: La = 0 for irreversible, La ⬍ 1 for favorable, La ⫽ 1 for linear, and La ⬎ 1 for unfavorable adsorption. The same holds for Fr in Freundlich’s isotherm. Freundlich adsorption isotherm constants for several compounds on several adsorbents/water systems at ambient temperature can be found in Table I.24, Appendix I. Dubinin–Raduskevish (DR) Due to the great significance of vapor adsorption on activated carbons, the DR isotherm, which is the most widely used in such cases, will be presented. It has several advantages (Dubinin, 1966): • • • •

predicts quite fairly the experimental data over a wide concentration range it includes the effect of temperature it is based on physical parameters easy application.

Dubinin assumed that the amount of vapor adsorbed (W) by an activated carbon, at a relative pressure P/Ps, is a function of the thermodynamic potential A: P  A ⫽ RT ln  s   P

(4.18)

where: R ⫽ the universal gas constant T ⫽ the absolute temperature Ps ⫽ the saturated vapor pressure at temperature T P ⫽ the partial pressure of the adsorbate. Dubinin reached the conclusion that the function was Gaussian after having studied the adsorption of simple organic compounds like benzene, and finally the classical expression of Dubinin and Radushkevich (the D–R equation) was derived (Dubinin, 1966; Cheng et al., 2004):   A 2  qe ⫽ qo exp ⫺k       

(4.19)

where qo is the maximum amount that can be adsorbed and k is a parameter characterizing the adsorbent. The parameter is called the “affinity” or “similarity” coefficient and expresses the ratio of the characteristic free energy of desorption of the test vapors to the free energy of desorption of the reference vapors. The affinity coefficients can be estimated by ratios of molar polarizabilities (Wood, 2001): ⫽

ref

(4.20)

270


The ratio of molar volumes in the liquid state can also be used: ⫽

V Vref

(4.21)

By definition, benzene is standardized as the reference compound for carbonaceous materials, and thus the value of its affinity coefficient is 1. The relevant parameters of the D–R isotherm can be found in paragraph I.4, Appendix I. Other isotherms Some common isotherms are the following: Langmuir–Freundlich isotherm Y⫽

XA B ⫹ (1⫺ B) X A

(4.22)

Y⫽

X B ⫹ (1⫺ B) X A

(4.23)

Redlich–Peterson isotherm

Sips isotherm X   Y ⫽  B ⫹ (1⫺ B) X 

A

(4.24)

where A and B are equilibrium constants. However, there is no reason to use more complicated isotherm models if two-parameter models, such as Langmuir and Freundlich, can fit the data well. It should be clarified that these models are only mathematical functions and that they hardly represent the adsorption mechanisms. Ion exchange Let us suppose that the solid phase is initially in the B form and that the liquid-phase ion is A. The ion exchange can be represented as (Helfferich, 1962) A ⫹ B A ⫹ B Then, the separation factor A⫺B is defined as 〈⫺〉 ⫽

qA CB YA X B ⫽ qBCA YB X A

(4.25)


271

with Yi ⫽

qi qmax

(4.26)

Ci Co

(4.27)

Xi ⫽

where i is A or B. Here, qmax is the MEL in equivalents per unit solid mass, which only in the case of complete exchange is equal to the REC. The REC is the “ceiling” of the solidphase capacity. Furthermore, Co is the initial concentration (normality) of the solution phase in equivalents per unit liquid volume, which is, in the case of ion exchange, constant throughout the exchanging process. If ion A is preferred, the separation factor is larger than unity, whereas if ion B is preferred, it is smaller than unity. The numerical value of the dimensionless separation factor is not affected by the choice of concentration units. Expressing all concentrations in equivalents, X A ⫹ X B ⫽ 1$ X B ⫽ 1⫺ X A YA ⫹ YB ⫽ 1$ YB ⫽ 1⫺ YA Then,

A⫺B ⫽

YA (1⫺ X A ) X A (1⫺ YA )

(4.28)

Then, the relationship between A⫺B and the Langmuir equilibrium parameter (La) is A⫺B ⫽

1 La

(4.29)

That is why La is also called the “separation factor.” Furthermore, it is clear that the constant separation factor A-B in an ion-exchange system means that in practice, this system obeys a Langmuiran equilibrium isotherm, in which La is of course constant. Another important equilibrium parameter is the distribution coefficient (i) defined as i ⫽

qi Ci

(4.30)

In practice, for the specific case of the distribution coefficient, mg/g units are used for q and mg/L for C. Thus, the units of are L/g.

272


Furthermore, the selectivity coefficient KA⫺B may be defined as follows: K A⫺B ⫽

qA Z B C B Z A

(4.31)

qB Z A C A Z B

where Zi is the absolute value of the ions charge. Here, concentration units are in eq/L. If the ion A is preferred, then the selectivity coefficient is larger than unity, whereas if ion B is preferred, it is smaller than unity. It is obvious that due to the exponents, different units result in different values of the selectivity coefficient. Ion-exchange equilibrium can be considered to be analogous to chemical equilibrium. From that point of view, the mass-action law can be used to express the state of equilibrium despite the fact that this law is defined exclusively for homogeneous systems. Derived this way, the so-called pseudo-equilibrium constant Ke is not really a constant, since it depends on the total concentration:

Ke ⫽

YA Z B X BZ A YBZ A X A Z B

q  ⫽ K A⫺B  o  C 

Z A ⫺Z B

(4.32)

o

However, if the valences of the exchanging cations are equal, the selectivity coefficient or pseudo-equilibrium constant is not affected by concentration. As already mentioned, one isotherm corresponds to a specific temperature in the case of adsorption or ion exchange of equal valence ions, whereas additionally, the same normality is required for the existence of only one isotherm in the case of ion exchange of different valence ions, due to the concentration–valence effect (Helfferich, 1962). The determination of the true equilibrium constant should be based on the thermodynamic activities (activity coefficients) of the species rather than concentrations. It is clear that the difficulties in the determination of activity coefficients also complicate the determination of the true equilibrium constant (Culfaz and Yagiz, 2004).

4.2 DESIGN OF ADSORPTION AND ION-EXCHANGE PROCESSES 4.2.1 Adsorption and ion-exchange kinetics in batch operations In this section, the basic theory required for the analysis and interpretation of adsorption and ion-exchange kinetics in batch systems is presented. For this analysis, we consider the transient adsorption of a single solute from a dilute solution in a constant volume, wellmixed batch system, or equivalently, adsorption of a pure gas. Moreover, uniform spherical particles and isothermal conditions are assumed. Finally, diffusion coefficients are considered to be constant. Heat transfer has not been taken into account in the following analysis, since adsorption and ion exchange are not chemical reactions and occur principally with little evolution or uptake of heat. Furthermore, in environmental applications,

4.2 Design of Adsorption and Ion-Exchange Processes

273

the objective is generally to remove a little amount of a pollutant from a gaseous or liquid stream, and thus, the overall heat released is very low, even if the heat per mole of adsorbed species is considerable, due to the low number of moles removed. In the following, two general cases are considered: • •

adsorption under conditions of constant or nearly constant solution concentration (infinite solution volume), and adsorption in batch with finite volume (finite solution volume).

In the adsorption in batch (second case), the concentration of the fluid varies from its initial to the equilibrium concentration. The partition ratio is used to discriminate between these two cases, which for the case of the solid phase initially free from the solute, is defined as (Perry and Green, 1999; Ruthven, 1984) w⫽

qe Ms Co ⫺ Ce ⫽ CoVo Co

(4.33)

where: qe ⫽ the equilibrium concentration expressed in solid phase in mass of solute per mass of the solid Ms ⫽ the mass of the solid Co ⫽ the fluid-phase initial concentration Ce ⫽ the equilibrium fluid-phase concentration Vo ⫽ the volume of the fluid phase. Generally, when w ⱖ 0.1, solutions for the second case are required. In the case of ion exchange, this ratio is somewhat differently defined (Helfferich, 1962): w⫽

QM Ms CoVo

(4.34)

Then, • •

for w ⬍⬍ 1, we have the infinite solution volume condition and for w ⬎⬎ 1, we have the finite solution volume condition

Here, QM represents the REC per unit mass of solid. Helfferich uses the total concentration of ions in equivalents for both phases. This difference is due to the different nature of adsorption and ion exchange. In ion exchange, the concentration in equivalents in both phases is constant throughout the process (stoichiometric phenomenon). On the other hand, in adsorption this is not the case. This is why in ion exchange, the total amount in equivalents in the solid phase QM is used, whereas in adsorption the equilibrium uptake qe is used.

274


Adsorption and isotopic exchange In the following analysis, adsorption models for solid diffusion control are applicable for isotopic exchange, i.e. exchange of isotopes, while in the case of liquid diffusion control and the intermediate case, only adsorption models for linear equilibrium can be used for isotopic exchange. Furthermore, as will be analyzed in practical applications, the adsorption models can also be used as a first approximation for ion-exchange systems, i.e. in the exchange of ions of different valences. Infinite fluid volume and solid diffusion control Practically, infinite solution volume condition (w t1 q(t2 ) ⬎ q(t1 ) C (t2 ) ⬍ C (t1 )

(4.111)

then

q(t2 ) ⫺ q(t1 ) ⫽⫺C t2 ⫺ C t1

V V ⫽ Co ⫺ C t2 ⫺ Co ⫺ C t1 m m

(4.112)

where Co ⫺ C (ti ) ⫽ U ti Co ⫺ C⬁

(4.113)


295

Then V m

(4.114)

q(t2 ) ⫺ q(t1 ) U (t2 ) ⫺U (t1 ) V ⫽ Co ⫺ C⬁ m t t

(4.115)

V dU ( t ) dq(t ) ⫽ Co ⫺ C⬁ m dt dt

(4.116)

q(t2 ) ⫺ q(t1 ) ⫽ U (t2 ) ⫺U (t1 ) Co ⫺ C⬁ Dividing by t ⫽ t2 – t1,

When t → 0,

Finally, in terms of kinetics rate, ⫺ri ⫽⫺

dC (t ) m dq(t ) dU (t ) ⫽ ⫽ Co ⫺ C⬁ dt dt V dt

(4.117)

In this expression, U(t) is relative rate of uptake and C- is relative to equilibrium, i.e. the sites available for ion exchange or adsorption for the specified ratio V/m. Thus, the absolute rate is a “coupled” result of kinetics and equilibrium. Note that in a solid diffusion–controlled process, U(t) is relative to the ease of movement of the incoming species in the solid phase (through Ds). Generally, a high diffusion coefficient means that the incoming species have the ability to make headway in any given direction relatively easily, and thus it is expected that the preference (selectivity) of the ion exchanger or adsorber for this species, and its equilibrium concentration, will be proportionally high. This general rule is has some exceptions (Inglezakis et al., 2004). A higher absolute rate for a species compared with another one does not necessarily mean a higher diffusion coefficient and higher preference for the incoming species, i.e. higher equilibrium concentration. The “ease of movement” has been expressed by Inglezakis et al. (2004) by the term “exchange site accessibility,” while the available sites for exchange/adsorption have been expressed by the term “exchange site availability.” In the framework of the so-called exchange site accessibility concept, an exchange or an adsorption site is characterized by two factors (Inglezakis et al., 2004): (a)

Its availability to the incoming species: The first factor determines if a site can be occupied by the incoming species and depends on the equilibrium behavior of the system, namely, the selectivity, distribution coefficients, and equilibrium concentrations.

296


(b)

Its accessibility to the incoming species: Accessibility is used to express the ease of movement of the species within the solid phase to reach the available site. It is closely connected to the kinetic behavior (diffusion coefficients) of the system.

Here, it has to be noted that the availability and accessibility depend not only on the characteristics of the site but also on the nature of the incoming species. As shown in relevant experiments, a site can be available for the incoming species but may not be easily accessible to them and vice versa. Design of a batch reactor system for adsorption and ion exchange Suppose that the system obeys the following equilibrium equation: Y ⫽ f (X )

(4.118)

This is a general type of adsorption isotherm. In the case of ion exchange, it is easy to correlate data in a way to have the same type of relationship for the ion that is removed from the liquid phase. When equilibrium is reached, Y⫽

qe qe ⫽ Yqmax qmax

(4.119)

Ce Ce ⫽ XCo Co

(4.120)

and X⫽

where Co is in mg/L and qmax is in mg/g. Here, qmax is the solid-phase concentration in equilibrium with Co for adsorption or the MEL in ion exchange. Using the equilibrium relationship (eq. (4.118)): qe ⫽ Yqmax ⫽ f ( X )qmax

(4.121)

Applying an overall material balance in the batch reactor and using eq. (4.120), qe m ⫽ (Co ⫺ Ce )V ⫽ (Co ⫺ XCo )V ⫽ (1⫺ X )CoV

(4.122)

where V is the volume of the fluid in L and m the mass of the solid phase in g. Then, equating (4.121) and (4.122), we have V qmax f ( X ) ⫽ m Co 1⫺ X

(4.123)


297

Using this equation, the ratio of the fluid volume to the solid mass needed to achieve a desired fluid-phase equilibrium concentration (X) can be calculated. We can achieve a lower liquid-phase concentration level by using a lower V/m ratio, using, for example, a higher amount of solid. Thus, equilibrium calculations result in the maximum V/m ratio that should be used to achieve the desired equilibrium (final) concentration. But, how much time do we need to achieve our goal in a batch reactor? This is a question to be answered by kinetics. By the definition of U(t), we have U (t ) ⫽

Co ⫺ C (t ) 1⫺ X (t ) ⫽ Co ⫺ Ce 1⫺ X

(4.124)

where X(t) is given by X (t ) ⫽

C (t ) Co

(4.125)

Then, to achieve C(t) equal to the equilibrium concentration for the specified V/m ratio, U(t) has to be equal to 1. Typically, this time for equilibrium is considerably high and impractical for most applications. For that reason, it is better to use V/m values lower than the maximum. By using several V/m values lower than the maximum, it is possible to calculate the corresponding equilibrium concentration X. Then, U(t) could be calculated for the desired X(t). Using these data, the kinetic model is applied to evaluate the time needed to achieve the desired U(t). Note that X in ion exchange and adsorption is the ratio of the species concentration to the initial concentration and is not equivalent to the conversion as defined in catalytic reactions, which is Co ⫺ C ( t ) Co

(4.126)

Co ⫺ C ( t ) ⫽ 1⫺ X Co

(4.127)

x⫽

and thus x⫽

Example 2 Meshko et al. (2001) studied the adsorption of a basic dye (Maxilon Goldberg GL EC 400% or MG-400) using zeolite in an agitated vessel of 0.5-L volume, under agitation (200 rpm) at ambient temperature (20 °C). The initial concentration of dye was 100 mg/L, the

298


volume of solution was 0.1 L, and the mass of zeolite used was 2 g. The zeolite properties were: particle size, 1–3 mm; porosity, 0.325; and solid density, 2.12 (g/cm3). The equilibrium followed the Langmuir isotherm with QM ⫽ 14.91 mg/g and K ⫽ 0.0434 L/mg. Some experimental kinetic data are given in Table 4.21 (approximate values). Meshko et al. (2001) used a homogeneous solid model taking into account both internal and external diffusion. They found that the adsorption of the dye had not been significantly affected by the agitation speed, which indicated that the process was solid diffusion–controlled. Furthermore, for the specified conditions, they found that kf ⫽ 6.66 ⫻ 10–5 m/s and Ds =10⫺12 m2/s. Use Patterson’s equation to model the kinetic data. Furthermore, propose a design for the agitated batch reactor. Solution Kinetics and equilibrium analysis To use Patterson’s model (eq. (4.52)), we first need the parameter w, and thus the equilibrium liquid-phase concentration Ce. The maximum loading of the zeolite particles for the specific initial concentration can be calculated by means of the equilibrium relationship (eq. (4.5)): qmax ⫽ QM

KCo ⫽ 12.12 mg/g 1⫹ KCo

Then using eq. (4.123), C V f (X) ⫽ o ⫽ 0.413 1⫺ X qmax m The function f(X) is the dimensionless equilibrium relationship, which for the Langmuir isotherm is (eq. (4.9)) f (X) ⫽

X ⫽ 0.413 1⫺ X La ⫹ 1⫺ La X

Table 4.21 Experimental data t (min)

qt (mg/g)

15 30 60 120 180

1 1.2 1.6 2.1 2.5


299

where La can be calculated by using eq. (4.8):

La ⫽

1 ⫽ 0.1873 1⫹ KCo

Then, a trial-and-error procedure is needed for estimating the equilibrium liquid-phase dimensionless concentration. In this way, we get X = 0.1 and thus

X⫽

Ce Ce ⫽ Co X ⫽ 9.975 mg/L Co

The parameter w is calculated by using eq. (4.33):

w⫽

Co ⫺ Ce ⫽ 0.9 Co

Then, the roots of eq. (4.58) are a = –3.477 and b = 0.777. To use the model, we need to transform the solid loading qt into U(t): U (t ) ⫽

Co ⫺ C ( t ) Co ⫺ Ce

where the liquid concentration C(t) can be calculated from the following relationship:

qt ⫽

Co ⫺ C ( t ) V m

In Table 4.22, t and the corresponding values of U(t) are given.

Table 4.22 U(t) versus t t (min)

U(t)

15 30 60 120 180

0.22 0.27 0.36 0.47 0.56

300


0.70

0.60

U(t)

0.50

0.40

U(t) , experimental 0.30

U(t) , model 0.20 0

2000

4000

6000

8000

10000

12000

t (sec)

Figure 4.19

The performance of the model.

By a trial-and-error procedure, using the value of U(t) and the model eqs. (4.52)–(4.58), the solid-phase diffusion coefficient is found to be 1.3 ⫻ 10–12 m2/s. This value is very close to the one given in the study of Meshko et al. The trial-and-error procedure can be done easily. By changing the value of Ds, the model predicts the values of U(t) for each t. The best value of Ds is the one that results in the lowest mean deviation between the experimental and the model values of U(t). In Figure 4.19, the performance of the model is shown. The average error is 3.1%. Reactor design Using the typical dimension ratios of an agitated vessel (see Section 3.3.5): 4  DT ⫽  V    Da ⫽ B⫽

1Ⲑ 3

⫽ 5.03 cm

DT ⫽ 1.68 cm 3 DT ⫽ 5 mm 10

Four baffles and a propeller with three blades could be used. Thus, for this specific system (eq. (3.104)), N Re ⫽

Da2 N 1000


301

For propellers, NP = 0.32 (see Table 3.4) and thus (eq. (3.107)) Ps ⫽

P N P N 3 Da5 ⫽ ⫽ 0.16 W/m 3 V V

Assuming that Df = 10–9 m2/s and using the Levins–Glastonburn correlation (3.118), the value of the mass transfer coefficient is found to be kf ⫽ 1.3 ⫻ 10⫺5 m/s. The difference between this value and the value given by Meshko et al. is due to the approximate value of Df used in this example. The Biot number is (eq. (4.104)) Bi ⫽

k f d p Co 2 Ds qmax

⫽ 82.41

which is much higher that the value of 30. So, it is reasonable to assume that intraparticle resistance dominates, as Meshko et al. (2001) found experimentally. By means of the Zwietering equation (3.114), with S = 6.5 for propellers (see Table 3.6), we find that the minimum rotational speed for complete suspension is 40.58 rps, much higher than the operating value of 3.33 rps (200 rpm). This means that the solid particles are not fully suspended in the liquid phase. Finally, using the Calderbank correlation (3.271), the minimum rotational speed for surface aeration is found to be 55.16 rps, which is fairly higher than the operation agitation speed, and thus surface aeration is avoided.

Some comments on dyes: Dyes can be a threat to both human health and environment. Focusing on the effect of dyes on human health, it should be pointed out that on exposure to an environment containing vapors of dyes, one would experience headaches and dizziness. After inhaling large portions of these vapors, limbs could get paralyzed and damage of the inner organs may take place. Nowadays, the risks associated with the use of such dyes have been widely recognized and many countries have posted many restrictions to their use. In Europe, the launch of a new standard called ISO 14000 has banned the import of all materials dyed with poisonous dyes. Example 3 A volume of 100 L of a solution containing 1000 ppm Pb2⫹ and minor amounts of other ions has to be treated. The desired final concentration is 100 ppm. The available adsorbent is a zeolite with a particle size of 1.3 mm, density of 2 g/cm3, and the REC is QM = 176 mg/g. Suppose that we have efficient agitation and solid diffusion is the controlling mechanism. Solid diffusion is about 6.4 ⫻ 10–10 cm2/s. Furthermore, the system obeys a favorable Langmuir isotherm with La = 0.03 and the maximum exchange level is qmax = 106 mg/g. Estimate the time needed in an agitated batch reactor for lowering the solution concentration to the value of 100 ppm for different masses of zeolite. Furthermore, propose a design for the reactor.

302


Solution The finite solution volume model for solid-diffusion control (Patterson’s model) will be used (eq. (4.52)). Following the procedure presented in the section Design of a batch reactor system for adsorption and ion exchange (eqs. (4.119)–(4.125)), we obtain the results shown in Table 4.23. In Figure 4.20, the kinetic curves for different zeolite amounts are presented. Lower V/m means a higher amount of solid. It is obvious that a faster process results in higher cost, as we have to use a larger amount of solid. The best solution is to study the system, using several combinations of time and solid mass to achieve the best operation design. Consider that 2.28 kg of solid are used. Geometrical dimensions of the system Suppose that we use a baffled cylindrical vessel and a flat-blade turbine with six blades. Given that the volume of the liquid to be treated VL ⫽ 100 L and using the analogies and equations of Section 3.3.5, we obtain impeller diameter Da ⫽ 16.77 cm, vessel diameter DT ⫽ 50.32 cm, impeller blade width W ⫽ 3.35 cm, liquid level HL ⫽ 50.32 cm, impeller level above the bottom equal to 16.77 cm, and B ⫽ 2.8 cm. Finally, it is desirable to use a vessel height HT somewhat higher than the Table 4.23 Kinetic data X

V/m (L/g)

m (kg)

U(t) for X(t) ⫽ 0.1

t (h) for X(t) ⫽ 0.1

Xmax = 0.1 0.05 0.02

(V/m)max = 0.093 0.071 0.044

mmin = 1.08 1.41 2.28

1 0.948 0.918

49.58 28.72 12.77

1000 m=1.08 kg m=1.41kg

800

C(t), mg/l

m=2.28 kg 600

400

200

0 0

500

1000

1500 2000 time (min)

2500

Figure 4.20 Kinetic curves.

3000

3500


303

liquid level, as a liquid vortex will result in an increase of the liquid level above the initial. So, a vessel with a 60-cm height is selected. The critical rotational speed of the impeller for complete suspension is approximately 541 rpm (9 rps) (eq. (3.114)). This rotational speed results in a Reynolds value of about 2.54 ⫻ 105, and thus the flow is turbulent. Here, it has to be noted that the mass transfer coefficient strongly depends on the rotational speed below the critical rotational speed needed for complete suspension, and weakly depends on the rotational speed above its critical value at which there are no particles remaining in rest for longer than 1–2 s in a fixed position. Verification of the controlling mechanism Zeolite particles are of irregular shape and a value of S ⫽ 0.65 could be used (see Table 3.13). Furthermore, as a first approach, the minimum mass transfer coefficient from Table 3.7 can be used. Thus, for density difference of 1 g/cm3 this minimum value is kf ⫽ 0.005 cm/s. Then, using Hand’s definition (eq. (4.105)), the minimum Biot number is 7.4 ⫻ 103. This number is much higher than the limit of 30 for solid diffusion control. Thus, we conclude safely that the controlling mechanism is solid diffusion, even in the absence of agitation. However, sufficient agitation should be provided to avoid any possible negative effect of liquid-film resistance. Using the appropriate equations and correlations of Sections 3.3.5 and 3.3.6, we can calculate the power consumption per unit volume of liquid and thus we can have an approximation of the actual mass transfer coefficient in the liquid film. NP = 6 for this type of impellers and thus kf is about 0.1 cm/s. It is obvious that kf is about 20 times the minimum value used above for the Biot number. A few comments on lead: The extensive use of tetraethyl lead (TEL) as a gasoline additive, for reducing engine knocking since 1922, constituted the greatest source of lead in the environment. Despite the fact that many experts were against its use and the negative effects on public health were known as early as the 1920s, the use of TEL continued till the decade of 1980 (Rosner and Markowitz, 1985). Lead is not an exception among heavy metals. It is very harmful even in very small amounts, since once absorbed into the body, it blocks certain enzymes, causing severe physiological or neurological consequences in the long term. The list of the hazardous effects of lead on human health is quite long: anemia and other blood disorders, damage to the nervous system and brain, kidney disease, and reproductive impairments in men and women. Overexposure to lead may cause birth defects, mental retardation, behavioral disorders, and even death in fetuses and young children. Example 4 Choy and McKay (2005) studied the removal of Cu2⫹ from aqueous phase using bone char in a batch reactor. The volume of the liquid was 1.7 L, the volume of the tank 2 L, and its diameter 0.13 m. A six-bladed flat impeller with a diameter of 0.065 m and a blade height of 0.013 m was used. Absorbent particles of 605- m diameter were used for the adsorption experiments. The adsorption isotherm at 20 °C was found to obey the Langmuir equation with K⫽ 69.2 L/mmol and QM ⫽ 0.709 mmol/g. For the kinetics experiments, Cu2⫹ solutions of 3.21-mM

304


Table 4.24 Experimental data t (min)

Ct (mmol/L)

25 50 100 150 180

2.3 2 1.6 1.3 1.18

concentration were mixed with 8.5 g of bone char. In Table 4.24, some experimental kinetic data are given (approximate values). Choy and McKay used a homogeneous surface diffusion model (HSDM) taking into account both external and internal transport, and found that the mean value of the solid diffusion coefficient is 3.72 ⫻ 10–9 cm2/s while kf = 6.06 ⫻ 10–4 cm/s. Assuming a solid diffusion–controlled process, use the Patterson equation and find the corresponding solid diffusion coefficient. Furthermore, find the impeller speed in order to have the reported value for kf. Solution To use Patterson’s model (eq. (4.52)), we first need the parameter w and thus the equilibrium liquid-phase concentration Ce. The maximum loading of the zeolite particles for the specific initial concentration can be calculated using the equilibrium relationship (eq. (4.5)): qmax ⫽ QM

KCo ⫽ 0.071 mmol/g 1⫹ KCo

Then (eq. (4.123)) C V f (X) ⫽ o ⫽ 0.910 1⫺ X qmax m The function f (X) is the dimensionless equilibrium relationship, which for the Langmuir isotherm is (eq. (4.9)) f (X) ⫽

X La ⫹ 1⫺ La X

where La is given by (eq. (4.8)) La ⫽

1 ⫽ 0.0045 1⫹ KCo


305

Then, the equilibrium liquid-phase dimensionless concentration is estimated following a trial-and-error procedure, and it is easy to find that X = 0.032. Thus, Ce ⫽ Co X ⫽ 0.103 mmol/L The parameter w is (eq. (4.33)) w⫽

Co ⫺ Ce ⫽ 0.9679 Co

The roots of eq. 4.58 are a = –3.691 and b = 0.787. Then U (t ) ⫽

Co ⫺ C ( t ) Co ⫺ C e

In Table 4.25, the values of U(t) and the corresponding values of t are shown. Again following a trial-and-error procedure, the solid-phase diffusion coefficient is found to be 1.82 ⫻ 10⫺9 cm2/s. This value is very close to the one given in the study of Choy and McKay. In Figure 4.21, the performance of the model is shown. The average error is 3%. The Biot number is (eq. (4.104)) Bi ⫽

k f d p Co 2 Ds qmax

⫽ 45.81

which is higher than the value of 30. So it is reasonable to assume that intraparticle resistance dominates. Calderbank–Moo–Young equation can be used for the mass transfer coefficient (eq. (3.120)):  P  kf ⫽ 0.13  s L   L 

0.25

Sc⫺2/3

Table 4.25 U(t) versus t

t (min)

U(t)

15 30 60 120 180

0.29 0.39 0.52 0.61 0.65

306


0.7

0.6

U(t)

0.5

0.4 U(t) , experimental 0.3 U(t) , model 0.2 0

2000

4000

Figure 4.21

6000 t (sec)

8000

10000

12000

The performance of the model.

The liquid-phase diffusion coefficient can be estimated from the Nernst–Haskell eq. (I-24) (see Appendix I): Dw ⫽

i RgT F2

For Cu2⫹, i = 53.6 cm2/ mol (Table I-8) and thus Df ⫽ 1.4 ⫻ 10⫺5 cm2/s and Sc ⫽ 641.88. For the given value of kf ⫽ 6.06 ⫻ 10⫺4 cm/s, the value of Ps is Ps ⫽ 0.16 W/m 3 ⫽

P N P N 3 Da5 ⫽ V V

Using the typical dimension ratios of an agitated vessel, B⫽

DT ⫽ 1.3 cm 10

Four baffles are selected. Thus, for this specific system, we can assume that NP ⫽ 4 (see Table 3.4), a value which is valid for NRe near or above 2 ⫻ 10⫺3. Then, the agitation speed N ⫽ 23.35 rpm, which results in a Reynolds number equal to N Re ⫽

Da2 N 1830

This justifies the use of the selected value of Np.


307

A few comments on copper: Copper is an element that has found many uses in the manufacture of electrical equipment due to its high electrical and thermal conductivity. Copper salts have also been used extensively as fungicides, in ceramics and pyrotechnics, and in many other industrial applications. Obviously, the utilization of copper by man has led to the reinforcement of its presence in the environment, with mining operations, solid waste, agriculture, and sludge from sewage-treatment plants being its primary anthropogenic releases. Gastrointestinal, hepatic, and renal effects with symptoms such as severe abdominal pain, vomiting, diarrhea, hemolysis, hepatic necrosis, hematuria, proteinuria, hypotension, tachycardia, convulsions, coma, and finally death, may result from the ingestion of grams of copper salts. 4.2.2 Fixed-bed operations for ion exchange and adsorption Introduction In the design of a sorption system, the knowledge of the main principles is important and many practical aspects have to be dealt with adequately. To begin with, the suitable sorbent material for our application has to be selected. It is highly desirable that its equilibrium properties such as capacity and selectivity are well known. Moreover, their dependence on temperature and component concentration has to be also understood. In addition, time and space have to be included in our design, since sorption operations are held in batch or fixed beds and exhibit a dynamic behavior. It means that steady-state operation is not the case in sorption systems in contrast to many other chemical operations that are independent of time. The fixed-bed performance has to be well related to adsorption equilibrium and rate behavior in a successful design. Many practical aspects have to be taken into account so that the process can be properly carried out. For example, dispersive phenomena at the bed scale as well as the aging mechanism have to be known and included in our design so that the behavior of the sorption system is controlled. Fixed bed is the most frequently used operation for ion exchange and adsorption, as it is the predominant way of conducting such sorption separations. Moreover, ion exchange and adsorption are common operations used for wastewater treatment. For example, in ion-exchange processes, zeolites and resins are used for the removal of heavy metals such as Pb2⫹, Cr3⫹, Fe3⫹, Cu2⫹, and ammonia (Baycal et al., 1996; Blanchard et al., 1984; Inglezakis et al., 2002; Cincotti et al., 2001; Babel and Kurniawan, 2003), while in adsorption processes, activated carbon, peat, iron oxide, kaolin, and fly ash are used for the removal of heavy metals and several organic substances from wastewaters, such as benzene, phenolic compounds, carbon tetrachloride, nitrophenol, acid dyes, and p-dichlorobenzene (Weber and Pirbazari, 1082; Chern and Chien, 2002; Babel and Kurniawan, 2003; Gupta et al., 1997, 2000; Ko et al., 2002; Sen et al., 2002). Furthermore, adsorption is frequently used for the removal of toxic compounds from gas streams, and some examples are the removal of phenanthrene (PAH) using coke (Murillo et al., 2004), the removal of sulfur dioxide and hydrogen sulfide using zeolites (Gupta et al., 2004; Yasyerli et al., 2002), and the removal of toluene (VOC) from a gas stream using activated carbon (Cheng et al., 2004).

308


1 0.9 0.8 0.7

C/Co

0.6 0.5 0.4 0.3 0.2 0.1 0 Breakpoint

Figure 4.22

Veff

Exhaustion

Breakthrough curve.

In Figure 4.22, a typical breakthrough curve is presented, where C/Co is the dimensionless exit concentration and Veff is the effluent volume. The breakpoint concentration is a selected limiting value (according to the needs of the treatment) where the operation should be stopped. The main aim when sizing adsorptive columns is the ability to predict the service time (or total effluent volume) until the column effluent exceeds breakpoint concentration. Then, from the practical point of view, the breakpoint volume is the most important information—it is the fluid volume that can be treated by the column until a prespecified breakpoint. This information could be derived from experiments conducted in laboratory columns and the scale-up goal is to “transfer” it to large unit or at least to evaluate the changes during scaling (see Chapter 6). Although the fixed-bed mode is highly useful, its analysis is unexpectedly complex. Fixed-bed operation is influenced by equilibrium (isotherm and capacity), kinetic (diffusion and convection coefficients), and hydraulic (liquid holdup, geometric analogies, and maldistribution) factors. Thus, a predictive modeling of fixed-bed systems requires extensive experimental information (laboratory and pilot-scale experiments) and complex mathematical tools; and in many cases, for complicated multicomponent solutions, the full modeling of the process is extremely difficult. For example, even in the simple cases of Pb2⫹, Cd2⫹, and Cu2⫹, and basic dyes removal from solution using clinoptilolite, peat, and activated carbon respectively, in fixed-bed operation, it is found that equilibrium parameters and/or solid diffusion coefficients seem to be unexpectedly influenced by contact time, making the modeling of the operation a complex task (Ko et al., 2003; Markovska et al., 2001; Inglezakis and Grigoropoulou, 2003). The situation is even more difficult considering the scale-up procedure. Scale-up is considering the geometric similarity (referring to linear dimensions), kinematic similarity (referring to motion), and dynamic similarity (referring to forces) at the same time. Frequently, it is not possible to achieve full similarity between different scales.


309

The effectiveness of a fixed-bed operation depends mainly on its hydraulic performance. Even if the physicochemical phenomena are well understood and their application in practice is simple, the operation will probably fail if the hydraulic behavior of the reactor is not adequate. One must be able to recognize the competitive effects of kinetics and fluid dynamics: mixing, dead spaces, and bypasses that can completely alter the performance of the reactor when compared to the “ideal” presentation (Donati and Paludetto, 1997). The main factor of failure in liquid-phase operations is liquid maldistribution, which could be related to low liquid holdup in downflow operation, or other design problems. These effects could be critical not only in full-scale but also in pilot- or even in laboratory-scale reactors. Basic design considerations Material balance Ion exchange and adsorption from aqueous solutions could be considered as isothermal and isobaric operations, while gas-phase adsorption operations could be nonisothermal and nonisobaric. However, in most environmental applications, the concentration of the gas-phase solute is low, and so the heat released and the pressure drop due to adsorption are low. Thus, it can be considered that isothermal and isobaric conditions are met (Murillo et al., 2004; Gupta et al., 2004; Cheng et al., 2004). Note that pressure drop could arise not only by the removal of a considerable amount of a species from a gaseous stream but also due to friction as gas is passed though the bed of solids. Pressure drop changes the fluid density, which in turn influences the volumetric flow rate and thus the superficial fluid velocity. The problem is analogous to the one encountered in catalytic reacting systems where the expansion factor due to the reaction and the pressured drop due to reaction and friction are in the picture (see Section 5.3.4). However, the pressure drop due to friction can be considered negligible in most practical applications. The material balance for fixed beds (see eq. (3.285)) is

DL

C 2C C ⫺ us ⫺ (⫺R) ⫽ 2 t z z

(4.128)

Rearranging and using eq. (3.289) for the rate expression –R in ion exchange and adsorption systems, we obtain DL 2C Z q Z C C ⫺ ⫺ b ⫽ us Z (z ⲐZ )2 (z ⲐZ ) us t us t

(4.129)

where the term Z/us is the fluid residence time (or contact time). Both C and q are dependent on time t and height Z. Hence, the expression is in the form of a partial differential equation. Rate equations There are two basic types of kinetic rate expressions. The first and simpler is the case of linear diffusion equations or linear driving forces (LDF) and the second and more rigorous is the case of classic Fickian differential equations.

310


Linear diffusion equations: This is the simplest case and is used extensively in the related literature (Perry and Green, 1999; Hashimoto et al., 1977; Cooney, 1990, 1993). The equations are the following: solid diffusion control:

q ⫽ K s ( qi ⫺ q ) t

fluid - film diffusion control:

q ⫽ K f (C ⫺ Ci ) t

(4.130)

(4.131)

where Ks and Kf are constants related to the local mass transfer coefficients. The subscript “i” corresponds to the concentrations in the solid–fluid interface. Differential diffusion equations: In this case, we have differential equations, one for each diffusion step (Perry and Green, 1999; Hall et al., 1966): q ⫽ K f (C ⫺ Ce ) t

(4.132)

 2 q 2 q  q ⫽ Ks  2 + t r r   r

(4.133)

q 1  C  ⫽ Kp 2  R2  t r  r r 

(4.134)

where Ks, Kp, and Kf are constants related to the local mass transfer coefficients. Eq. (4.132) is for the case of fluid-film diffusion control, eq. (4.133) for solid diffusion control, and eq. (4.134) for pore diffusion control. Pore diffusion is similar to solid diffusion; it, however, represents the fluid diffusion in pores and is considered to be an intermediate diffusion step, between fluid-film and solid diffusion. For the case of fluid-film diffusion, the equation is the same as the LDF equation. However, here, the equilibrium concentration (Ce) is used in place of the interface concentration (Ci) to illustrate one significant point. These concentrations are equal only in the case of solid diffusion as the controlling mechanism; otherwise they are different. For the case of differential diffusion equations, only arithmetic solutions are possible and will not be presented in this book. Equilibrium The physical process (reaction) of adsorption or ion exchange is considered to be so fast relative to diffusion steps that in and near the solid particles, a local equilibrium exists. Then, the so-called adsorption isotherm of the form q = f (Ce) relates the stationary and mobile-phase concentrations at equilibrium. The surface equilibrium relationship between the solute in solution and on the solid surface can be described by simple analytical equations (see Section 4.1.4). The material balance, rate, and equilibrium equations should be solved simultaneously using the appropriate initial and boundary conditions. This system consists of four equations and four unknown parameters (C, q, q, and Ce).


311

Predictive models Mass transfer–controlled systems—“diffusion” models and single resistance to diffusion Predictive models can be used to model the removal process and determine the controlling step of the whole phenomenon. In most cases, particle diffusion within the solid matrix is the controlling mechanism. But the controlling step is strongly dependent on fluid linear velocity, since at low velocities the fluid-film diffusion step could be the controlling mechanism. Thus, as a first step, the lowest possible linear velocity could be used for the evaluation of the controlling step. To use simplified models, the following assumptions should be met. 1.

2.

Plug flow: In this case, the first term in eq. (4.128) is neglected. This assumption holds only if the axial Peclet number of the bed (PeL) is greater than about 100 (Levenspiel, 1972). For packing materials of irregular shape, such as zeolites and activated carbon, and 0.5 mm particle size, a bed of 50-cm height is sufficient, for superficial velocities higher than about 0.2 cm/s (Inglezakis et al., 2001). However, by using upflow operation, this value could be by far lower. Generally, by using upflow mode, the quality of the flow is much better, especially at low velocities. Constant pattern condition: This condition reduces the mass balance equation (4.128) to the simple relation: C/Co=q/qmax (see the section A look into the “constant pattern” condition). Practically, the constant pattern assumption holds if the equilibrium is favorable, and at high residence times (Perry and Green, 1999; Wevers, 1959; Michaels, 1952; Hashimoto et al., 1977). However, the constant pattern assumption is “weak” if the system exhibits very slow kinetics (Wevers, 1959).

Various simplified models under the above assumptions have been proposed and analyzed in the related literature and are in the form of either arithmetic or analytical solutions. In the following, simplified models will be presented under the two commonly applied assumptions, namely, the constant pattern and plug-flow assumptions. The following dimensionless parameters are defined (Perry and Green, 1999): ⌳⫽

b qmax Co

(4.135)

 V  t ⫺ o   Q T⫽  ⌳Vo   Q 

(4.136)

kf auVo Q

(4.137)

Nf ⫽ Np ⫽

15Dp (1⫺)Vo ro2Q

(4.138)

312


Ns ⫽

15Ds ⌳Vo ro2Q

(4.139)

where t is the time, Vo the total bed volume, the bed voidage, Q the volumetric flow rate, b the bulk density of the bed, and qmax the operating capacity (in mass of solute per unit mass of the solid when the fluid-phase concentration is in mass of solute per unit volume of the fluid). The operating capacity is the solid-phase concentration at equilibrium with the initial fluid-phase concentration for adsorption systems and the MEL for ion-exchange systems. In general, the operating capacity is experimentally more accurately determined under dynamic conditions, i.e. in a fixed-bed system. Subscript “f” refers to the fluid, “s” to the solid, and “p” to the pore fluid-phase resistance. The following equations constitute the approximate solutions of the fixed-bed model under the constant pattern and plug-flow assumption for the favorable Langmuir isotherm and linear driving forces (Perry and Green, 1999): ln (X )⫺ La ln (1⫺ X ) ⫹1 1⫺ La

(4.140)

1  La ln (X )⫺ ln (1⫺ X )  ⫺1  S  1⫺ La 

(4.141)

N f (T ⫺1) ⫽

N s (T ⫺1) ⫽ where

S⫽

0.894 1⫺ 0.106 La 0.5

(4.142)

Eq. (4.140) is for liquid-film diffusion control and eq. (4.141) for solid diffusion control. The following equation is a solution of the fixed-bed model under the constant pattern and plug-flow assumption, for fluid-film diffusion control and the favorable Freundlich isotherm (Fleck et al., 1973):

N f (T ⫺1)⫽ 1⫹ ln (X )⫺

⫽

1⫺Fr   Fr ln  1⫺ X Fr  ⫹ 1⫺ Fr  

Fr ⬁ Fr ∑ Fr ⫺1 k⫽1 k  k (1⫺ Fr )⫹ Fr 

(4.143)

(4.144)

In Figure 4.23, the model results for solid diffusion control (eq. (4.141)) and two different values of the Langmuir constant (La) are presented. In Figure 4.24, the model results for solid diffusion and liquid-film diffusion control (eq. (4.140)) for La ⫽ 0.5 are presented. From Figure 4.23, it is clear that the more favorable the isotherm (lower value of La), the steeper the breakthrough curve, and thus, the better the performance of the operation.


313

1

0.8

C/Co

0.6

0.4 La = 0.6 0.2

La = 0.2

0 -10

-5

0

5

10

Ns(T-1)

Figure 4.23

Solid diffusion control model for La = 0.2 and 0.6. 1

0.8

C/Co

0.6

0.4 Ns(T-1) 0.2

Nf(T-1)

0 -8

-3

2

7

N(T-1)

Figure 4.24

Solid and film diffusion control models for La = 0.5.

From Figure 4.24, it is clear that in the case of liquid-film control, the curve has a tail in the beginning, while in the case of solid diffusion control, the tail is at the end. Finally, two models based on nonlinear driving forces will be presented. The first one covers the case of a pore diffusion control and the second one the case of solid diffusion control. Both models hold for the Langmuir-type isotherm. For the case of

314


pore diffusion control, the model equations are the following (Vermeulen and Quilici, 1970): N p (T ⫺1) ⫽

 1  La La ⫺2 1⫺ (1⫺ La ) X ⫺ ln 1 ⫹ ln 2  ⫹ I c  pore  1⫺ La 1⫺ La  1 ⫽

1⫹ 1⫺ (1⫺ La ) X 1⫺ 1⫺ (1⫺ La ) X 1⫺ (1⫺ La ) X ⫹ La

2 ⫽

1⫺ (1⫺ La ) X ⫺ La

I c ⫽ 2.44 ⫺ 2.15La pore ⫽

(4.145)

1 La ⫹1.83(1⫺ La )0.92 2

(4.146)

(4.147)

(4.148) (4.149)

For solid diffusion control, the model equations are the following (Inglezakis, 2002b): N s (T ⫺1) ⫽

1 solid

  1 1⫺ La 2 ( 1 ⫹ 2 ⫹ 3 ) ⫹ I c

(4.150)

  1 1 ⫽ ln  2  (1⫹ La ) ⫺ 2 LaX ⫺ (1⫺ La ) X 

(4.151)

1⫹ La   X⫹  1⫺ La  2 ⫽ La ln    1⫺ X   

(4.152)

3 ⫽ 2 La 2 ln X

(4.153)

solid ⫽

La

1.5

1 ⫹1.688(1⫺ La )

(4.154)

I c ⫽⫺106.67 La 5 ⫹177.82 La 4 ⫺114.38 La 3 ⫹ 31.604 La 2 ⫺ 4.5323La ⫺ 0.8408 (4.155)


315

Both models are good approximations, especially when we are interested in the first part of the breakthrough curve, which is of great importance in industrial applications. “Diffusion-type” models have been used for the adsorption of lead, copper, p-nitrophenol, phenol, p-bromophenol, p-toluene sulfonate and dodecyl benzene sulfonate on activated carbon (Hashimoto et al., 1977; Xiu and Li, 2000; Chen and Wang, 2004; Crittenden and Weber, 1978), and ion exchange of ammonia, lead, and other heavy metals on clinoptilolite (Inglezakis and Grigoropoulou, 2003; Cincotti et al., 2001; Semmens et al., 1978; Cooney et al., 1999). A look into the “constant pattern” condition Under the assumption of plug flow, the material balance in a fixed bed (eq. (4.128)) can be written as follows:

us

q X Y X ⫹ b max ⫹ ⫽0 t z C o t

(4.156)

In this equation, the partial derivatives with respect to time are positive, whereas the one with respect to length is negative. Furthermore, X ⫽ C/Co and Y ⫽ q/qmax. From the above equation, we have

    us z ⫽⫺  t  ⫹ q max b Y   C o X  where

z ⬍0 t

(4.157)

(4.158)

This partial derivative is the velocity of the concentration front in the bed. The constant pattern assumption presupposes that this velocity is constant, or in other words, is independent of the solution concentration. This means that all points on the breakthrough curve are “traveling” in the bed under the same velocity, and thus a constant shape of this curve is established (Wevers, 1959). According to the above equation, this could happen only if (Perry and Green, 1984) Y ⫽ const. Y ⫽ c 1 X ⫹ c 2 Y ⫽ X X

(4.159)

316


This relationship is the constant pattern condition. According to the literature, a criterion for the constant pattern assumption is the following (Perry and Green, 1999; Wevers, 1959):  qmax   us   C   Z  o

⫺1

 X    ⬎⬎ 1 t z

(4.160)

The first term is the ratio of maximum loading of solid for a specific inlet concentration to that concentration, whereas the second term is the space velocity (the reciprocal of the residence time), and the third term is the slope of the breakthrough curve. Thus, the constant pattern condition is achieved for • • • •

dilute solutions materials of high capacity for the solute high residence time, i.e. low flow rate and deep beds favorable equilibrium.

Mass transfer–controlled systems—“diffusion” models and combined resistances This model covers the case where we have combined resistances to diffusion (fluid-film and solid diffusion). In this case, the concentration in the main phase of the fluid (bulk concentration) is different from the one at the interface due to the effect of the fluid film resistance. The following equations can be used for Langmuir and Freundlich equilibrium equations (Miura and Hashimoto, 1977). The solutions of the fixed-bed model are the following: Langmuir isotherm ⫺ X ⫽

1 ⫽

1 1 2 1 ⫹ 1⫹ 1⫹

La La 1 ln X i ⫺ ln 1⫺ X i ⫺ ln  La ⫹ 1⫺ La X i  ⫺ ln La ⫹1 1⫺ La 1⫺ La 1⫺ La 2 ⫽

La 1 ln 1⫺ X i ⫺1 ln X i ⫺ 1⫺ La 1⫺ La ⫽ 1⫺ 0.1921⫺ La 3

(4.161)

(4.162)

(4.163) (4.164)

Freundlich isotherm 1 1 1 2 ⫹ 3 1 ⫹ 1⫹ 1+ 1⫹ ⫹

(4.165)

Fr Fr 2 IA ln X i Fr⫺1 ⫺1 ⫹1⫹ Fr ⫺1 + Fr ⫺1

(4.166)

− X ⫽ 1 ⫽


2 ⫽

1 1 ln 1⫺ X i1⫺Fr ⫹ IB Fr ⫺1 + Fr ⫺1 3 ⫽ Fr ⫺1⫹

Fr (I A ⫹ I B ) Fr ⫺1

⫽ 0.808 ⫹ 0.192Fr

317

(4.167)

(4.168) (4.169)

⬁

1 1 ⫺ n n Fr ⫹ Fr n⫽1

IA ⫽ ∑

(4.170)

⬁

1 n⫽1 n n1⫺ Fr ⫹1

IB ⫽ ∑

(4.171)

where ⫽

ks a u  Z   t ⫺  u b (1⫹1/ ) 

(4.172)

ks a u Z 1⫹1/ u

(4.173)

X ⫽

ks a u ⫽

15Ds b ro2

(4.174)

⫽

qmax Co

(4.175)

⫽

k f au ks a u

(4.176)

au ⫽

3 (1⫺) ro

(4.177)

Here, qmax is in mass of solute per unit mass of solid and Co is in mass of solute per unit volume of fluid. For practical use, the infinite series IA and IB are shown in Figure 4.25. The interface relative concentration Xi is related to the respective bulk relative concentration X as follows: Langmuir X⫽

( La ⫹ ) X i ⫹ (1⫺ La ) X i 2 ( ⫹ ) La ⫹ (1⫺ La ) X i

(4.178)

318


4 3.5 3 2.5 2 1.5 1 0.5

IA IB

0 0

0.1

0.2

0.3

0.4

Figure 4.25

Freundlich X⫽

0.5 Fr

0.6

0.7

0.8

0.9

1

Values of IA and IB.

X i ⫹ X i Fr ⫹

(4.179)

Model equations give only the relation of Xi versus – X, and when the direct relationship between X and – X is required, it is desirable to represent Xi explicitly by X. For the Langmuir isotherm,

Xi ⫽

⫺b ⫹ b2 ⫺ 4 ac 2a

(4.180)

where a ⫽ (1⫺ La), b ⫽ ( La ⫹ ) ⫺ ( ⫹ )(1⫺ LaX ) and c ⫽⫺La( ⫹ ) X Note that in the case of a solid diffusion control, Xi is replaced by X in the analytical solutions. The presented analytical solutions were developed primarily for solid diffusion control, also termed “surface diffusion kinetics.” However, the same equations can be used as approximations for pore diffusion as the controlling intraparticle diffusion step, if ksau is set equal to 15Dp(1⫺)/ro2 . However, there is no explicit relationship for the Freundlich isotherm. In this case, for a desired value of X, several Xi values should be checked, and thus an extra calculation step is needed, i.e. an iteration procedure (Figure 4.26).


Langmuir

319

Freundlich

Set (X)

Assume (Xi) NO

Calculate (Xi)

Calculate (X) Is the desirable (X) ?

Calculate θτ-Xτ

YES

Calculate θτ-Xτ

Figure 4.26

The use of Miura’s models.

The limiting cases of the analytical solutions for external fluid-film mass transfer controlling ( ; 0) and solid diffusion controlling ( ; - ) are the following: Langmuir 0 ⫺ C ⫽

La 1 ln X ⫺ ln (1⫺ X ) ⫹1 1⫺ La 1⫺ La

(4.181)

and ⬁

1 1  La  ln 1⫺ X ⫺1 ln X ⫺ ⫺ X ⫽  1⫺ La 1⫺ La 

(4.182)

Freundlich 0 ⫺ X ⫽ 1⫹ ln X ⫺

Fr 2 Fr 1⫺Fr Fr IA ln 1⫺ X ( )/ ⫺ 1⫺ Fr 1⫺ Fr

(4.183)

and ⬁

1 1 1  ln 1⫺ X 1⫺Fr ⫹ IB  ⫺ X ⫽  Fr ⫺1   Fr ⫺1

(4.184)

320


1

0.8

X

0.6

0.4

ζ=0.0001

0.2

ζ=1 ζ=10000 0 -5

-3

-1

Θτ-Xτ

1

3

5

Figure 4.27 Examples of theoretical breakthrough curves calculated from the analytical solutions for the Freundlich isotherm (Fr ⫽ 0.5).

In Figure 4.27, some examples of theoretical breakthrough curves calculated from the analytical solutions for the Freundlich isotherm (Fr ⫽ 0.5) are presented. As is clear, the curve corresponds to the case of equal and combined solid and liquid-film diffusion resistances ( ⫽ 1) which is between the two extremes, i.e. solid diffusion control ( ⫽ 10,000) and liquid-film diffusion control ( ⫽ 0.0001). Example 5 Hashimoto et al. (1977) studied the removal of DBS from an aqueous solution in a carbon fixed-bed adsorber at 30 °C. The dimensions of the bed were D = 20 mm and Z ⫽ 25.1 cm. Carbon particles of 0.0322-cm radius were used, with 0.82 g/cm 3 particle density, and 0.39 g/cm 3 bulk density. The concentration of the influent stream was 99.2 mg/L and the superficial velocity was 0.0239 cm/s. The fixed bed was operated under upflow condition. Furthermore, the isotherm of the DBS–carbon system at 30 °C was found to be of Freundlich type with Fr ⫽ 0.113 and K F ⫽ 178 (mg/g)(L/mg) 0.113 . Finally, the average solid-phase diffusion coefficient was found to be 2.1 ⫻ 10 –10 cm2 /s. The approximate value of 10 –9 m2/s could be used for DBS liquid-phase diffusion coefficient. Using the Miura–Hashimoto model, calculate the time needed to reach a breakpoint concentration of 9.92 mg/L (10%). According to the experimental results given by Hashimoto et al., the time needed for the specified breakpoint concentration is 226 hr. What is the result if the solid diffusion control approximation is used?


321

Solution The dimensionless numbers needed for further calculations are Rep ⫽ Sc ⫽

dp us

⫽ 0.154

⫽ 1002 Df

The mass transfer coefficient in the liquid film can be calculated using the correlation of Williamson (eq. (3.346)): Sh ⫽ 2.4 0.66 Rep0.34 Sc 0.33 ⫽ 8.11 where b ⫽ (1⫺ ) p ⫽ 0.524 and thus Sh ⫽

kf dp Df

kf ⫽ 0.00126 cm/s

The solid-side mass transfer coefficient is (eq. (4.174)) ks a u ⫽

15Ds b ro2

⫽ 1.18 ⫻10⫺6 g/s cm 3

The equilibrium relationship is used for the determination of the maximum loading of the solid phase (eq. (4.14)): qo ⫽ 178Co0.113 ⫽ 229.24 mg/g Then, using eq. (4.175), ⫽

qmax ⫽ 3016.58 cm 3 /g Co

322


and thus, from eq. (4.176), ⫽

k f au ⫽ 15.61 ks a u

where (eq. (4.177)) au ⫽

3 ⫺1 (1⫺ )⫽ 44.31 cm ro

The parameter XT is (eq. (4.173)) XT ⫽

ks a u Z ⫽ 3.528 1+1/ us

For Fr = 0.113, ⫽ 0.8297 (eq. (4.169)), IA ⫽ 1.7 and IB ⫽ 1.08 (Figure 4.25). Then, for C/C0 ⫽ X ⫽ 0.1, and for eq. (4.165), we have T – XT ⫽ ⫺1.217 (Xi ⫽ 0.066). Finally, from eq. (4.172), T ⫽

ks a u  Z  t ⫺  t 225 h us  b (1⫹1/ ) 

This is only a difference of 0.45% from the experimental value. Constant pattern approximation does not negatively affect the final result. For solid diffusion control, ζ→∞ and for this case, from eq. (4.184), T – XT = –1.277 (Xi = 0.1) and t 227 h or a difference of 0.25% from the experimental value. It is interesting to check the Peclet number of the fixed bed. The Reynolds number is 0.154, and for this low value, the most appropriate correlation is that of Chung (eq. (3.314)). The resulting particle Peclet number is 0.39 and thus, the bed Peclet number is 151.98, which is fairly high, and we can assume that the plug-flow condition is assured.

A few comments on dibromostyrene: Known also as brominated styrene, it has been used as a flame retardant in the manufacture of thermoplastics. Although it does not seem to be toxic for the amounts present in the environment, it might have a teratogenic potential if large amounts of it are orally administered, as developmental studies in rats and rabbits have shown. Example 6 Rivero et al. (2004) studied the removal of HCrO4⫺ (Cr6⫹) from polluted groundwater in a fixed bed at 24 °C. The dimensions of the bed were D = 1.64 cm and Z = 13.25 cm. An


323

anion exchange resin (Lewatit MP 64) of 0.59-mm particle diameter was used, with 0.654 g/cm3 bulk density. The influent stream contained 1.273 mmol/L of HCrO4– and the superficial velocity was 0.134 cm/s. Furthermore, the isotherm of the HCrO4⫺–resin system at 24 °C was found to be of Freundlich type with Fr ⫽ 0.194 and KF ⫽ 1.815 (mmol/g)(l/mmol)0.194. Finally, the solid-phase diffusion coefficient was found to be 1.43 ⫻ 10–8 cm2/s. The approximate value of 1.19 ⫻ 10⫺9 m2/s could be used for the HCrO4– liquid-phase diffusion coefficient. Finally, assume the value of 0.41 for bed porosity. Using the Miura–Hashimoto model, calculate the time needed to reach a breakpoint concentration of 0.636 mmol/L HCrO4– (50% of inlet concentration). According to the experimental results given by Rivero et al. (2004), the specified breakpoint concentration is reached after 1000 BV (bed volumes). Solution The Reynolds and Schmidt numbers are Rep ⫽

Sc ⫽

dp us

⫽ 0.79

⫽ 842 Df

Using the correlation of Williamson (eq. (3.346)),

Sh ⫽ 2.4 0.66 Rep0.34 Sc 0.33 ⫽ 11.35 then Sh ⫽

kf dp Df

kf ⫽ 0.00229 cm/s

The solid-side mass transfer coefficient is (eq. (4.174)) ks a u ⫽

15Ds b ro2

⫽ 0.000161 g/s cm 3

where (eq. (4.177)) au ⫽

3 (1⫺ ) ⫽ 60 cm⫺1 ro

324


Using the equilibrium relationship (eq. (4.14)): qo ⫽ 1.815Co0.194 ⫽ 1.90 mmol/g Then, from eq. (4.175), ⫽

qmax ⫽ 1494 cm 3 /g Co

The mechanical parameter is (eq. (4.176)) ⫽

k f au ⫽ 0.571 ks a u

And thus, from eq. (4.173), XT ⫽

ks a u Z ⫽ 8.65 1⫹1/ us

For Fr = 0.194, = 0.8452 (eq. (4.169)), IA = 1.75 and IB = 1.14 (Figure 4.25). Then, for C/C0 = X = 0.5, and from eq. (4.165), we have T – XT = –0.0403 (Xi = 0.0179). Then, from eq. (4.172), T ⫽

 Z  ks a u t ⫺  t 26.71 h BV 971.5 us  b (1⫹1/ ) 

The Miura model predicts the experimental value found by Rivero et al. (2004) with a deviation of –2.85%. A few comments on chromium (VI): As mentioned before, the contamination of water by heavy metals is a major environmental problem. Chromium is a metallic element with oxidation states ranging from chromium (–II) to chromium (⫹VI). In particular, there is great concern about Cr (⫹VI), as there is sufficient evidence for its carcinogenicity in humans. It rarely occurs naturally and its presence in the environment is anthropogenic, such as chromate production, chromate pigment production, and the chromium-plating industry. Specifically, the emissions of Cr6⫹ constituted 35% of the estimated 2.700–2.900 tn of chromium emitted into the atmosphere annually from anthropogenic sources in the United States (ATSDR, 2000). However, chromium is not dangerous only in this form. Irritation to the nose, nosebleeds, and ulcers and holes in the nasal septum may appear if high levels of chromium (VI) are inhaled. Moreover, the ingestion of large amounts of chromium (VI) may lead to stomach upsets and ulcers, convulsions, kidney and liver damage, cancer, and in the worst case, even death (ATSDR, 2000).


325

The Reynolds number is 0.78, and the resin particles can be assumed to be spherical. Then the most appropriate correlation is that of Chung (eq. (3.314)). The resulting particle Peclet number is 0.51, and thus, the bed Peclet number is 114.93, which is fairly high, and plug flow condition can be considered to take place. Example 7 Murillo et al. (2004) studied the adsorption of phenanthrene (polycyclic aromatic hydrocarbon –PAH) from helium as carrier gas on a coke fixed-bed adsorber, at 150 °C. The isotherm of the phenanthrene–coke system at 150 °C was found to be of Freundlich type with Fr = 0.161 and KF = 1.9 (mol/kg)(m3/mol)0.161. The isotherm has been derived for phenanthrene concentrations between 1.71 ⫻ 10⫺4 and 1.35 ⫻ 10⫺2 mol/m3. Finally, the average solid-phase diffusion coefficient, calculated from several experimental runs, was found to be 6.77 ⫻ 10⫺8 cm2/s. Suppose that coke is available with particle density of 1 g/cm3 and particle size of 1 mm. The bed diameter is 0.2m, its height is 1 m, and the bed porosity is 0.5. The gas stream to be treated is helium containing 4.74 ⫻ 10⫺3 mol/m3 phenathrene. Using the Miura–Hashimoto models, calculate the time needed to reach a breakpoint of 10% of the inlet concentration under a flow rate of 31,400 cm3/s. Solution The Reynolds and Schmidt numbers are Rep ⫽

Sc ⫽

dp us

⫽ 4.56

⫽ 9.42 Df

Then, using the correlation of Wakao for gases (eq. (3.351)): Sh ⫽ 2 ⫹1.1Sc 0.33 Rep 0.6 ⫽ 7.73 then Sh ⫽

kf dp Df

kf ⫽ 18.01 cm/s

The solid side mass transfer coefficient is ks a u ⫽

15Ds b ro2

⫽ 0.000203 g/s cm 3

326


where au ⫽

3 (1⫺ ) ⫽ 30 cm⫺1 ro

Using the equilibrium relationship: qo ⫽ 1.9Co0.161 ⫽ 0.802 mol/kg then ⫽

qmax ⫽ 1.69 ⫻105 cm 3 /g Co

The mechanical parameter is ⫽

k f au ⫽ 15.73 ks a u

And thus: XT ⫽

ks a u Z ⫽ 32.29 1⫹1/ us

For Fr ⫽ 0.161, ⫽ 0.839, IA ⫽ 1.73, and IB ⫽ 1.12. Then, for C/C0 ⫽ X ⫽ 0.1, T ⫺ XT ⫽ ⫺1.309 (Xi ⫽ 0.0697). Then, T ⫽

 Z  ks a u t ⫺  t 22.53 h us  b (1⫹1/ ) 

A few comments on polycyclic aromatic hydrocarbons: A large number of polycyclic aromatic hydrocarbons (PAHs) are formed mainly during the incomplete combustion of coal, oil and gas, garbage, or other organic substances. In air, they can condense on dust particles, making them very dangerous to human health. Their presence in the aquatic environment should be attributed to the discharges from industrial and wastewater treatment plants. In water, PAHs also tend to attach to solid particles and gradually settle to the bottoms of lakes or rivers. In soil, they remain for a period of weeks to months until they are broken down by the action of microorganisms. During that period, they may move through soil to contaminate underground water (ATSDR, 1995).


327

The Reynolds number is 4.56 and using the Edwards–Richardson correlation (eq. (3.317)) the particle Peclet number is found equal to 2.18 and thus, the bed Peclet number is 2179, which is fairly high and the plug-flow condition is assured. Surface reaction models–Thomas—BDST model The Thomas model is also referred to as the bed-depth-service-time (BDST) model (Low et al., 1999; Lehmann et al., 2001; Zulfadhly et al., 2001; Walker and Wetherley, 1997; Ko et al., 2000). The BDST approach is based on the irreversible isotherm model by Bohart and Adams (Ko et al., 2000). However, in practice, the model is used for any equilibrium type. Furthermore, this simplified-design model ignores both the intraparticle (solid) mass transfer resistance and the external (fluid-film) resistance so that the adsorbate is adsorbed onto the solid surface directly. This means that the rate of adsorption is controlled by the surface reaction between the adsorbate and the unused capacity of the adsorbent (Zulfadhly et al., 2001). This model is essentially a constant pattern model (Ko et al., 2000). The expression by Thomas for an adsorption column is given as follows (Kapoor and Viraraghavan, 1998; Rao and Viraraghavan, 2002): Ce Co

1   1⫹ exp  (qmax M ⫺ CoVtot )   Q

(4.185)

where: Ce, Co ⫽ the effluent and inlet solute concentrations qmax ⫽ the maximum adsorption capacity M ⫽ the total mass of the adsorbent Q ⫽ the volumetric flow rate Vtot ⫽ the throughput volume ⫽ the Thomas rate constant, volume/mass time. The Thomas model is also applicable to the design of ion-exchange columns (Kapoor and Viraraghavan, 1998). The Thomas equation constants qmax and values can be obtained from the column data and can be used in the design of a full-scale adsorption bed. This equation is simple since it can be used in its linear form:  qmax M Co C ln  o ⫺1 ⫽ ⫺ Vtot Q Q   Ce

(4.186)

Essentially, the above model is empirical as in real systems, both solid and fluid-film resistances play an important role in the adsorption process. An improved BDST model is found elsewhere (Ko et al., 2000, 2002). Finally, qmax and could be flow rate–dependent parameters (Walker and Weatherley, 1997). The Thomas model has been used for the sorption of heavy metals using fungal biomass, bone char, chitin, and goethite (Kapoor and Viraraghavan, 1998; Lehmann et al.,

328


2001; Zulfadhly et al., 2001; Ko et al., 2000, 2002; Sag and Actay, 2001), dyes adsorption using metal hydroxide and activated carbon (Netpradit et al., 2004; Walker and Weatherley, 1997; Ko et al., 2002), and phenol biosorption using biomass (Rao and Viraraghavan, 2002). Wheeler–Jonas model for VOCs adsorption The Wheeler–Jonas equation is used for adsorption of VOCs using carbons. This equation is of the same form of the Thomas model with some modifications:

t br ⫽

qe M qe b  Co ⫺ Cbr  ⫺ ln CoQ kvCo  Cbr 

(4.187)

where: tbr ⫽ the time to reach the breakthrough concentration (Cbr) kv ⫽ the overall adsorption rate coefficient, time–1 qe ⫽ the equilibrium adsorption capacity of the carbon for a given vapor. The Dubinin–Raduskevish isotherm (DR) can be used for the determination of qeq. The overall adsorption rate coefficient can be estimated using the following equations. Jonas and Rehrmann (1974) proposed a model derived for 1% breakthrough fraction: k ⫽ 111.6us0.5 dp⫺1.5

(4.188)

where us is the superficial velocity in cm/s and dp is the particle diameter in cm. Lodewyckx and Vansant (2000) proposed a model derived for 0.1% breakthrough fraction:

k ⫽

48 0.33us0.75 dp1.5

(4.189)

Wood and Stampfer (1993) proposed the following correlation: ⫺1

   C ⫺ Cbr    0.063 ⫺ 0.0058 ln  o      Cbr     1 k ⫽  ⫹ 0.027 0.000825 ⫹  Pe   us        

(4.190)

where the superficial velocity is in cm/s, concentration in ppm, and Pe is the molar polarization of the adsorbate in cm3/mol.


329

“Diffusion-type” models are two-parameter models, involving kf or Ds and La, while BDST models are one-parameter models, involving only , as qmax is an experimentally derived parameter. The determination of La requires the whole experimental equilibrium curve, and in case of sigmoidal or other non-Langmiur or Freundlich-type isotherms, these models are unusable. From this point of view, BDST models are more easily applied in adsorption operations, at least as a first approximation. Example 8 Cheng et al. (2004) studied the adsorption of toluene (VOC) in a fixed bed of activated carbon fibers at 298 °C and 1 atm. The inlet concentration of toluene was 17.36 mg/m3 and the carrier gas was air. Toluene obeys the Dubinin–Raduskevish isotherm with k ⫽ 1.101 ⫻ 10–9 mol2/J2 and qo ⫽ 57.73 kg/m3. The following data are given. Toluene: affinity coefficient ⫽ 1 (approximate value), molar polarizability Pe ⫽ 3.11 ⫻ 104 cm3/mol, density ⫽ 0.8669 kg/m3, MB ⫽ 92.14 g/mol and saturated vapor pressure at the given temperature = 0.375 ⫻ 104 Pa. Carbon fibers: particle radius r ⫽ 13 ⫻ 10–3 mm, particle density p ⫽ 87 kg/m3. Bed: diameter D ⫽ 6 mm, height H ⫽ 8 mm, mass of solid phase ⫽ 15 mg and interstitial velocity u ⫽ 17 m/s. According to the experimental data, the first appearance of toluene in the exit stream is at about 50 min, while after 100 min the exit concentration is 10% of the inlet one. Calculate the time needed for the same breakpoint concentration using the Wheeler–Jonas equation and Wood and Stampfer equation for the evaluation of kv. Moreover, examine the Wheeler–Jonas equation for the specified experimental conditions. On the basis of the results, predict the breakpoint time for lower interstitial velocities down to 1 m/s. Solution In the absence of more experimental data and for the purposes of the present example, we assume that the first appearance of toluene just after 50 min corresponds to an exit concentration of 0.01%, which is practically close to zero. This value will be used as the breakpoint concentration in the following calculations. The Dubinin–Radushkevich isotherm is (eq. (4.19))   A 2  qe ⫽ qo exp ⫺k        where from eq. (4.18), P  A ⫽ RT ln  s   P

330


These equations are applied for the determination of the equilibrium solid loading qe for the specified inlet concentration. To do this, the partial pressure of toluene at inlet conditions is needed. This pressure is calculated by using the ideal gas law: P ⫽ CRT ⫽ 0.46 Pa Then, A ⫽ 2.23 ⫻ 104 and qe ⫽ 33.4 kg/m3. In the Wood and Stampfer correlation (4.190), the bed voidage is needed in order to calculate the gas superficial velocity. Given the dimensions of the fixed bed, its volume is 0.226 cm3 and thus, the bulk density is

b ⫽

M ⫽ 66.35 kg/m 3 Vbed

And thus, ⫽ 1⫺

b ⫽ 0.24 p

Then, the superficial gas velocity is 403.54 cm/s and the kinetic coefficient kv is 4.1 ⫻ 104 1/s. Finally, using the Wheeler–Jonas equation (4.187) for Q ⫽ 1.14 ⫻ 10⫺4 m3/s, the breakpoint time is found to be approximately equal to 43 min, which is close to the experimental value (50 min). From Table I-15 of Appendix I, , we find that the diffusion coefficient of toluene in air is 8.7 ⫻ 10⫺6 m2/s. Then, using the properties of air at 25 °C (Table I-6, Appendix I), we find that Sc ⫽ 1.74 and Rep ⫽ 6.92, and using the Edwards–Richardson correlation (eq. (3.317)) the particle Peclet number is found to be 1.98 and thus, the bed Peclet number is 609.2, which is fairly high, and plug-flow condition can be assumed. In Figure 4.28, the model predictions are plotted for different breakpoint concentrations. Note that while the model works quite well for low Cbr, 0.01% in our case, it fails to represent the data for higher values. For example, for Cbr ⫽ 1.7 mg/L (10%), it predicts a breakpoint time of only 47.2 min instead of 100 min, which is the approximate experimental value. This is an expected result as normally, this kind of “breakpoint” models are designed to work at relatively low breakpoint concentrations. On the other hand, by setting the “first appearance” at lower values of exit concentration, the model gradually predicts a much lower “first appearance” time than the experimental one. Thus, it seems that a breakpoint or “first appearance” concentration in the vicinity of 0.01–1% is adequate in order to have representative results (filled squares). In Figure 4.29, the breakpoint concentration for Cbr ⫽ 1% is presented for different values of the interstitial velocity. It is evident that the relationship between the interstitial velocity and the breakpoint time is not linear, and thus, for values lower than about 5 m/s, the increase of the breaktime is sharp.


331

10 1 35

37

39

41

43

45

47

49

Cbr (%)

0.1 0.01 0.001 0.0001 0.00001 tbr (min)

Figure 4.28

Model predictions (Cbr versus tbr).

1000

tbr (min)

800

600

400

200

0 0

5

10

15

20

u (m/s)

Figure 4.29

Breakpoint concentration for Cbr = 1% for different values of interstitial velocity.

A few comments on toluene: Toluene (C6H5CH3) is extensively used in gasoline to boost the octane number; it is also the raw material in the production of other chemicals such as benzene, benzyl chloride, phenol, cresols, vinyl toluene, and TNT. It also finds use in the production of paints, coatings, adhesives, etc. 99% of the total toluene emissions are released into the atmosphere, primarily from gasoline evaporation and automobile exhaust. Industrial plants that use toluene as solvent constitute another great source of toluene (ATSDR, 1989). Due to its presence in cigarette smoke and in the releases from common household products, it is also a pollutant of indoor atmospheric environment. Being chemically and

332


biologically degradable in the environment, it is not expected to reach high concentrations over time in the environment (ATSDR, 1989). Since toluene is principally released into the atmosphere, it is obvious that inhalation is the primary route of exposure to it for the general population, and for occupationally exposed individuals. CNS dysfunction and neurological impairment are the main adverse acute health effects of toluene in humans. At concentrations in the range 50–1500 ppm and for 3–8 h exposure, individuals may suffer drowsiness, impaired cognitive function, incoordination, and irritation of the eyes and throat. Increased concentrations may lead to more severe symptoms like nausea, staggering gait, confusion, extreme nervousness, and even insomnia lasting for several days (NTP, 1990). At concentrations in the range 10,000–30,000 ppm, toluene may cause narcosis and death (WHO, 1985). Helfferich–Glueckauf model for ion exchange Although the models presented in the previous sections can be successfully applied in practice in ion-exchange systems, the Helfferich–Glueckauf approach will also be presented, which is developed especially for ion-exchange systems (Helfferich, 1962). The Helfferich–Glueckauf approach relies basically on Glueckauf’s approach. The concept of “effective plates,” originated from the theory of distillation and first applied to chromatography, is of primary importance in these models. Equilibrium theories also employ this concept and include the plate height as an empirical quantity. However, in the following approach, the plate height is calculated from fundamental data and is incorporated in a typical kinetic theory, i.e. an approach using kinetics and not only equilibrium relationships. The approach is applicable to the so-called self -harpening boundaries in columns, i.e. in the case of favorable equilibrium (Figure 4.31). Furthermore, the equations can be applied only at steady state conditions, i.e. when the boundaries attain a steady shape. In other words, here we have models that are based on the constant pattern condition. Suppose that the solid phase is initially in say, form B and that the liquid phase ion is A. The ion exchange can be presented as (Helffecrich, 1962) A ⫹ B A ⫹ B The sharpness of a boundary between two counterions, A displacing B, depends on their separation factor (A–B) and on the operation conditions. At steady state conditions, the spread of the boundary is given by Hp ⫹ Ho   1 1 1 ⫺ A⫺B ln ⫹1 ⫹ ln  ⫺ ⫺ ⫺ 1 1 2 1 X X  A⫺B A A⫺B A  H f ⫹ H o  A⫺B 1 1 1 ln ln ⫺ ⫺1  2  A⫺B ⫺1 X A A⫺B ⫺1 1⫺ X A 

zA ⫽

(4.191)


333

1

0.8

A

B

C

D

XA

0.6

0.4

0.2

0 -25

-20

-15

-10

-5

0

5

10

15

20

25

zA (cm)

Figure 4.30 Shape of a self-sharpening boundary as calculated by Helfferich-Glueckauf model, for several combinations of diffusion resistances and separation factor. A: A–B ⫽ 10, Hp ⫽ 6 cm, and Hf ⫽ 1cm, B: A–B ⫽ 2, Hp ⫽ 6 cm, and Hf ⫽ 1cm, C: A–B ⫽ 10, Hp ⫽ 1 cm, and Hf = 6 cm, D: A–B ⫽ 2, Hp ⫽ 1 cm and Hf ⫽ 6 cm. For all cases, Ho⫽ 1 cm.

Solution

z

ZA

0

Figure 4.31

XA 1

The self-sharpening boundary.

where zA is the spread of the boundary in cm (see Figure 4.31) used for the rest of the parameters, and H o ⫽ 1.64ro

(4.192)

334


HP ⫽

b qo / Co

0.142ro2 us 2 Ds ( b qo / Co ⫹ )

(4.193)

2

 q /C  0.266ro2 us Hf ⫽  b o o   b qo / Co ⫹  Df (1⫹ 70ro us ) aA⫺B ⫽

qA CB YA X B ⫽ qBCA YB X A

XA ⫽

CA Co

(4.194)

(4.195)

(4.196)

In the above equations, Ho, Hp, and Hf are the plate-height contributions due to the finite particle size, solid diffusion, and liquid-film diffusion. respectively. CGS units are used in these equations. Obviously, the bigger the height of the plate, the higher the resistance to the diffusion and the lower the uptake rate. The shape of the boundary and that of the concentration profile (front), as it travels though the bed, are identical. We want a small width of boundary, i.e. the boundary to be close to zA ⫽ 0. It is the case when the separation factor is high and the resistances to diffusion are weak (Figure 4.30). For the derivation of the model, it is assumed that (a) (b) (c) (d)

the separation factor is constant (favorable Langmuiran-type equilibrium isotherm), the linear-driving-force rate laws for ion exchange are valid, the spreads caused by solid and liquid film are additive, and the flow in the fixed bed is ideal (plug flow

In the original work of Helfferich, qo is considered to be equal to the concentration of the fixed ionic groups in equivalents per unit volume of the ion exchanger, which in the course of the process will be completely used by the ion initially present in the solution phase A. However, in practice, this level is never achieved. Instead, the operating capacity, derived by employing experimental runs, can be used. Thus, qo is the operating capacity, i.e. the maximum (A) uptake of the solid phase achieved in bed, expressed in mass (or equivalents) per unit mass of the solid phase. This value could be lower than the one measured in batch reactors and defined as the MEL and even lower than the REC. Furthermore, Co is the initial concentration of B (normality) in the liquid phase, in equivalents per unit volume of liquid. This concentration, expressed in equivalents per unit volume of the liquid, is constant throughout the ion-exchanging process, as ion exchange is a stoichiometric process. In the case of displacement of a single species, i.e. the exchange of a single ion from the liquid phase for a single ion in the solid phase, the effluent volume at which a given concentration XA emerges from the column is   q Veff ⫽ A  b o ⫹  Z ⫺ zA C   o

(4.197)


335

or in terms of time,   q A  b o ⫹  Z ⫺ zA   Co t( XA ) ⫽ Q

(4.198)

where A is the cross-sectional area of the bed, Z is its height, and Q is the volumetric flow rate. The presented model is very convenient when we want to calculate the breakthrough capacity, i.e. the solid loading obtained until breakpoint. We have (Helfferich, 1995) ZAqo ZAqbr ⫹ zA Aqo z qbr qo ⫺ qo A Z zA   qbr qo  1⫺   Z

(4.199)

where qbr is the breakthrough capacity in mass of solute per unit mass of the solid. As the total mass of solute passed through the bed till breakpoint is VbrCo, where Vbr is the exit solution volume until breakpoint, M out ⫽ Vbr Co ⫺ qbr Vo b (4.200) where Vo is the bed volume and Mout is the mass of the solute that was not adsorbed by the bed solid and thus “escaped”, until breakpoint. That way, we can calculate the mean exit concentration:

Cavr,br ⫽

M out Vbr

(4.201)

The mean exit concentration Cavr,br is always lower than the breakpoint exit concentration. Finally, the so-called degree of column utilization can be calculated:

⫽

qbr q

(4.202)

Equilibrium-limited systems In the case of unfavorable equilibrium, the local equilibrium analysis can be applied. Essentially, assuming local equilibrium between the fluid and the solid phase, the mass transport step is neglected or is considered to have a minimal effect

336


on the system evolution when compared to that of unfavorable equilibrium. In this case, the equation of continuity reduces to (Perry and Green, 1999) dY dF ( X ) ⫽T ⫽T dX dX

(4.203)

where Y ⫽ F(X) is the equilibrium relationship between Y and X. The limits of validity of this equation can be found by setting X ⫽ 0 (for T minimum) and X ⫽ 1 (for T maximum). Using the local equilibrium analysis, the isotherm of a system can be found from breakthrough experiments using the following equation: 1

Y ⫽ ∫ T dX 0

(4.204)

In the case of favorable equilibrium, the local equilibrium analysis predicts that at T = 1 the concentration X will rise instantly from 0 to 1 (ideal step change). This situation is ideal and does not correspond to real situations, as when a system exhibits favorable equilibrium, the mass transfer is always the controlling step. In the special case of ion exchange and unfavorable equilibrium, i.e. A⫺B < 1, with A originally in the solution, under the condition of sufficiently long bed, Walter’s solution could be used. Walter’s equation is a special case of the Thomas model for arbitrary isotherm and the kinetic law equivalent to a reversible second-order chemical reaction (Helfferich, 1962):

X⫽

R⫺ R T R ⫺1

(4.205)

ⱕR . The dimensionless effluent concentration X is zero for T⬍1 R and equal to 1 for T/R for T ⱖR . In this equation, R. is the average value of the reciprocal separation factor 1/A⫺B. For a Langmuir isotherm, A⫺B and La are related: A⫺B ⫽

1 La

(4.206)

In this case, R ⫽ 1 La. Finally, T is the throughput ratio defined in eq. (4.136). Experimental methods for the determination of the controlling mechanism in a fixed-bed operation For the determination of the controlling mechanism, in the case of mass transfer–controlled systems, the following method can be used (Inglezakis, 2002b). This approximate method requires only the experimental bed data, and specifically, the set of exit concentrations and


337

the corresponding elapsed times. Then the (T – 1) versus C/Co curve is constructed, where C is the exit concentration, Co is the inlet concentration, and T is the dimensionless time modulus. According to Perry and Green (1999), if the experimental data are plotted in N (T – 1) versus C/Co graph, the C/Co at which N (T – 1) and thus T – 1 equals zero is called the “stoichiometric point,” and is independent of the volumetric flow rate. This is the point where the amount of solute that has “leaked” past the reference point in the bed exactly equals the residual unfilled capacity of the solid contained before that point. The characteristic C/Co versus N (T – 1) curves are shown in Figure 4.32. This point is always lower than about C/Co = 0.7, regardless of the controlling mechanism (fluid film, pore, or solid diffusion or a combination of them), for both Langmuir and Freundlich isotherms, which are frequently used in adsorption and ion-exchange systems (Perry and Green; Hall et al., 1966; Fleck et al., 1973). Furthermore, the values of (C/Co)stoich (corresponding to the stoichiometric point) for solid diffusion control are between 0.51 and 0.70 and the more favorable equilibrium leads to higher values of (C/Co)stoich. The opposite holds for fluid-film control where the corresponding (C/Co)stoich values are between 0.31 and 0.5. The above method for the determination of the controlling step is of course approximate and qualitative, and can be used only if the model cannot be applied due to experimental or other problems. Using the approximate “diffusion” models (eqs. (4.140) and (4.141)), the Xstoich versus La is shown in Figure 4.33.

1

0.8

C/Co

0.6

0.4 Ns(T-1) 0.2

Nf (T-1)

0 -5

-3

-1

1

3

5

N(T-1)

Figure 4.32 Characteristic C/Co versus N (T – 1) curves for solid diffusion control (dotted line) and fluid-film diffusion control (La = 0.2).

338


0.65 0.6

Xstoich

0.55 0.5 0.45 S

0.4

F 0.35 0

0.2

0.4

0.6

0.8

1

La

Figure 4.33 Stoichiometric point curves (S: solid diffusion control, F: fluid-film diffusion control).

For the estimation of the controlling mechanism, Miura and Hashimoto defined the mechanical parameter as (Miura and Hashimoto, 1977) ⫽

k f au K qmax Co

K⫽

(4.207)

15Ds b

au ⫽

ro2

(4.208)

6(1⫺) dp

(4.209)

According to their analysis, if is zero (practically much lower than 1), then the fluid-film diffusion controls the process rate, while if is infinite (practically much higher than 1), then the solid diffusion controls the process rate. Essentially, the mechanical parameter represents the ratio of the diffusion resistances (solid and fluid-film). This equation can be used irrespective of the constant pattern assumption and only if safe data exist for the solid diffusion and the fluid mass transfer coefficients. In multicomponent solutions, the use of models is extremely difficult as numerous data are required, one of them being the equilibrium isotherms, which is a time-consuming experimental work. The mathematical complexity and/or the need to know multiparameters from separate experiments in all the “diffusion” models makes them rather inconvenient for practical use (Juang et al., 2003). If modeling or other approximate methods are not applicable, then a number of experiments should be conducted in order to examine the effect of superficial velocity on the performance of the bed, and more specifically on the breakpoint volume. Keeping the same contact time and particle size, one can study the effect of linear velocity by changing just the length of the bed accordingly, and in this way examining the controlling step. For solid


339

diffusion control there is no effect of the linear velocity on the kinetics in the bed, while for film-diffusion control the kinetics will be affected. In fact, this is a scale-up procedure. From these experiments, a lower limit of the linear velocity can be found, above which the controlling mechanism is the solid diffusion control. Then, for higher linear velocities, which are expected in large-scale units, it is guaranteed that the controlling step is unaltered. Derivation of basic experimental data Experimentally, it is valuable to determine the following: the maximum adsorption capacity of the solid (qmax), the diffusion coefficients of the solutes in the solid phase (Ds), and the equilibrium isotherm. Theoretically, the equilibrium isotherm, the maximum adsorption capacity, and the solid diffusion coefficient for a specific ion-exchange or adsorption system are independent of the experimental method used for their determination and independent of flow conditions (Helfferich, 1962; Lieu and Weber, 1981; Chen and Wang, 2004; Fernandez et al., 1996). However, experimental results obtained in ion-exchange and adsorption systems showed that the maximum adsorption capacity measured in a fixed bed is different from that measured in a batch system, and it might be flow-rate dependent (Inglezakis et al., 2002; Inglezakis and Grigoropoulou, 2003; Hlavay et al., 1982; McKay and Bino, 1985; McLaren and Farquhar, 1973; Netpradit et al., 2004; Ko et al., 2003; Sen et al., 2002). Furthermore, as shown in the cases of adsorption on activated carbon and the removal of Cr (III) using zeolite NaX, the equilibrium isotherm is influenced by the type of the reactor used for its measurement (fixed-bed and batch reactor) (Weber and Wang, 1987; Barros et al., 2006). Finally, the diffusion coefficient may be different when measured in fixed beds or batch systems, and in some cases is found to be flow-dependent (Inglezakis and Grigoropoulou, 2003; Yoshida et al., 1984; Weber and Smith, 1987; McKay and Bino, 1985; Ko et al., 2003; Markovska et al., 2001). All these observations seem to be a result of the limiting (low) contact time in fixed beds and of the different concentration gradients in fixed beds and batch reactors (Inglezakis and Grigoropoulou, 2003; Ko et al., 2003). It must be recognized that batch reactors do not approximate the hydrodynamic and contaminant removal patterns of fixed-bed (column) reactors (Weber and Smith, 1987). Another reason responsible for such differences between batch and fixed-bed parameters is the lack of adequate mechanical strength of particles (compressed particles due to flow pressure) and the permeability of particles to fluid flow (Fernandez et al., 1996). Obviously, this case is possible only when resins are used. The maximum adsorption (or ion-exchange) and breakthrough capacity can be measured using the experimental breakthrough curve (C versus Veff) by integration (Perry and Green, 1999; Helfferich, 1962): Vtot    Vtot ⫺ ∫ X (Veff )dVeff  Co   Vf qo ⫽ bVo Vbr    Vbr ⫺ ∫ X (Veff )dVeff  Co   Vf qbr = bVo

(4.210)

(4.211)

340


where Vf is the effluent volume until the first appearance of the solute in the exit stream, Vbr the effluent volume until breakpoint, and Vtot the effluent volume until the exit solute concentration is equal to its inlet concentration. The dependence of qo on residence time has been noticed and modeled elsewhere, in the case of zinc and acid dyes adsorption by bone char and activated carbon, respectively (Ko et al., 2002). The following equations were successfully applied: qo,bed ⫽ qo,max 1⫺ exp⫺t res

(4.212)

0.5 qo,bed ⫽ qo,max 1⫺ exp⫺tres

(4.213)

where qo,bed and qo,max are the bed maximum capacity and the real maximum capacity (measured in batch reactor systems), tres the residence time, and a system-specific constant. It is obvious that if the residence time is infinite, the bed maximum capacity is equal to the real maximum capacity, which is theoretically expected, as noted elsewhere (Inglezakis and Grigoropoulou, 2003). These equations have been also tested in Pb2⫹ adsorption by clinoptilolite (zeolite) and showed satisfactory results (Inglezakis, 2002b). In conclusion, the maximum adsorption capacity should be measured in fixed-bed experiments under dynamic conditions, and if models are applicable, diffusion coefficients should be also determined in fixed-bed apparatus. Due to the fact that the equilibrium isotherms require extended data series and thus are time-consuming experiments, the latter are quite difficult to be conducted in fixed-bed reactors and from this point of view, it is more practical to evaluated equilibrium isotherms in batch reactor systems. Then, it is known that when applying fixed-bed models using an equilibrium isotherm obtained in batch-type experiments, the equilibrium discrepancy (if it exists) can be compensated by a different estimate for the solid diffusion coefficient (Inglezakis and Grigoropoulu, 2003; Weber and Wang, 1987). Example 9 Wastewater containing 100 ppm Pb2⫹ and minor amounts of other ions has to be treated (20°C). The maximum allowable exit concentration is 10 ppm. The available adsorbent is a zeolite of particle size 2 mm ( S ⫽ 0.8), particle density 2 g/cm3, and bulk density 1 g/cm3. Suppose that solid diffusion is the controlling mechanism. Solid diffusion is measured and found to be about 10–9 cm2/s. Furthermore, the system obeys the favorable Langmuir isotherm with La ⫽ 0.1. The MEL is qmax ⫽ 200 mg/g. The available amount of the zeolite is 100 kg. (a) (b)

Propose an optimum design for this operation by using an LDF model. Compare the LDF model with Helfferich’s model for upflow operation and Qrel ⫽ 5 BV/h.

Solution Model analysis: The simple LDF model for solid diffusion control will be used, namely eq. (4.141). For the specified system with La = 0.1, the Ns(T – 1) versus (C/C0) is shown in Figure 4.34.


341

1

0.8

C/Co

0.6

0.4

0.2

0 -2

-1

0

1

2

3

4

Ns(T-1)

Figure 4.34 Model plot.

From the definition of T (eq. (4.136)), it is clear that since t⫺

Vo P0 Q

T should be positive. Using this constraint for T, it can be seen that

T P0 T ⫺1P⫺1 N (T ⫺1)P⫺ N N P⫺ N (T ⫺1)min This minimum value of N(T – 1) corresponds to the lower value of X, i.e. 0. However, since the model solution, eq. (4.141), is asymptotic to the X – axis, the lower X to be used is 0.01. This dimensionless exit concentration can be viewed as the “first appearance” concentration. This restriction provides the maximum allowable relative flow rate or the minimum residence time in order to have the first appearance of the solute for T ⱖ 0. For La ⫽ 0.1, [N(T⫺1)]min⫽ ⫺1.2311 (eq. (4.141)). Geometrical dimensions of the fixed bed: The volume of the bed is

Vo ⫽

Ms ⫽ 0.1 m 3 b

342


Then Vo ⫽

D2 D2 Z⫽ 4 4

D3  Z  Z ⫽ D     4  D D

To avoid large-scale liquid maldistribution, the aspect ratio Z/D should be greater than 5 (see eq. (3.324)). If we choose the value of 10, then  4V D⫽ o 

1/3

 D     Z 

⫽ 0.234 m

and thus Z ⫽ 2.34 m. At the same time, D ⫽ 117 ⬎ 30 dp Z ⫽ 1168 ⬎⬎ 150 dp Thus, for the selected D and Z, large-scale maldistribution is prevented (see eq. (3.324)). Relative flow rate limits: According to Ruthven (1984), a maximum linear velocity should not be exceeded in order to avoid extended friction between the packing material in both down- and upflow operations. This velocity is 0.8 times the minimum fluidization velocity for upflow operation and 1.8 times the same velocity for downflow operation (for further analysis see Chapter 6). Using eq. (3.451), the minimum fluidization velocity for the specific system is 2.36 cm/s. Thus, the maximum allowable velocities for down- and upflow operations are 4.24 and 1.89 cm/s, respectively. In terms of the relative flow rate, the corresponding values are 65.35 and 29.13 BV/h. These values are fairly high for ionexchange systems and are rarely used in practical applications. Apart from these “hydraulic” limits, one more limit comes from the model as analyzed before. We found that N P⫺ N (T ⫺1min P1.2311 or, by using eq. (4.139), 15Ds Vo ro2Q

P1.2311

The relative volumetric flow rate is (eq. (3.103)) Vo 1 ⫽ Q Qrel


343

we have Qrel ⱕ

15Ds 1.2311ro2

Qrel ⱕ 6.66 BV/h

As will be analyzed in the following section, a minimum relative flow rate arises for downflow operation. Hydraulic analysis and controlling mechanism: For the determination of the mass transfer coefficient, and the case of wastewater treatment, the correlation proposed by Chern and Chien (eq. (3.345)) is used. As a criterion, the Biot number as defined by Hand et al. (eq. (4.105)) is used. For the evaluation of the particle Peclet number and the liquid holdup, the correlations proposed by Inglezakis et al. are used, i.e. eqs. (3.313) and (3.332), respectively. The Biot number, liquid holdup, and bed Peclet number for downflow operation versus relative volumetric flow rate are presented in Figure 4.35. It is obvious that for the whole flow-rate range, the rate-controlling mechanism is expected to be the solid diffusion control (Bi > 41). Furthermore, the flow can be characterized as ideal plug flow for flow rates above 2.15 BV/h, where PeL is higher than about 100. However, the liquid holdup is very low (56.83%) and this could be proved a serious problem, even with the use of a liquid distributor at the top of the bed. In order to have a satisfying liquid holdup, i.e about 80%, the relative flow rate should be about 5.62 BV/h. Then, by means of a liquid distributor at the top of the bed, it is possible to achieve a holdup near 100%. Thus, for downflow operation the limits of the relative flow rate are (BV/h) 5.62 ⱕ Qrel ⱕ 6.67 On the other hand, in upflow operation, PeL is higher than 140 for the whole relative flowrate region up to the value of 6.66 BV/h. At the same time, the liquid holdup is always

140 120 100 80 60

Bi

40

h% Pe L

20 0

1

2

3

4

5

6

7

Q(BV/h)

Figure 4.35 Biot number, liquid holdup, and bed Peclet number for downflow operation versus relative volumetric flow rate.

344


100% (in the absence of large-scale maldistribution). Thus, for upflow operation, there is only an upper limit for the relative flow rate (BV/h): Qrel ⱕ 6.67 Resulting performance: In Figure 4.36, the performance of the fixed bed is presented for flow rates up to 6.66 BV/h. The measure of the performance of the bed is the breakpoint time and the corresponding treated volume of the waste as calculated by utilizing the model equation (4.141). The choice of the appropriate flow rate depends on the wastewater volume to be treated and on the desired service time, and as presented in a following section, on the degree of utilization of the fixed-bed material. In upflow operation, a low flow rate could be used, for example, 0.5 BV/h and in this case, as much as 188 m3 of the wastewater could be treated in 157 days. On the other hand, if the bed is operated under the highest flow rate of 6.66 BV/h, the treated volume is 48 m3 and the service time 73 h. It should be noted that in downflow operation the volume that can be treated is between 48 and 72 m3, due to the limits on the relative flow rate, as analyzed in a previous section. Comparison with Helfferich’s model: The parameters of the model, for 5 BV/h relative flow rate, are the following: Ho ⫽ 0.164 cm, Hf ⫽ 26.37 cm, and Hp ⫽ 230.19 cm (eqs. (4.192)–(4.194)). The models are compared in Figure 4.37. It is evident that the models are in very good agreement. However, in the region of the breakpoint they deviate. For X ⫽ 0.1, the LDF model results in 86.06 m3, while Helfferich’s model in 70 m3, i.e. almost 23% difference. In Figure 4.38, the difference (%) of these two models versus the exit concentration is presented. As can be seen, the difference is lower than 7% for Cout greater than about 20 ppm, i.e. X > 0.20. The differences at low X are due to the fact that Helfferich’s approach 10000 t (h) V(m3 ) 1000

100

10 0

Figure 4.36

1

2

3 4 Q(BV/h)

5

6

7

The performance of the fixed bed for flow rates up to 6.66 BV/h.


345

100

Cout (ppm)

80 60 40 Helfferich

20

Perry 0 0

200

400

600

Veff (m3)

Figure 4.37

Comparison of LDF and Helfferich models for Qrel ⫽ 5 BV/h.

25

% difference

20 15 10 5 0 0

50

100

150

-5 Cout (ppm)

Figure 4.38

The difference (%) of LDF and Helfferich’s model versus the exit concentration.

(eq. (4.191)) takes into account the resistance in the liquid film and thus the characteristic tail at the beginning of the breakthrough curve is apparent. The utilization of the fixed bed: The value of zA for X ⫽ 0.1 is 151.68 (eq. (4.191)). Then, by using eq. (4.199) and for Qrel ⫽ 5 BV/h, the breakpoint loading of the solid phase is z   qbr ⫽ qo  1⫺ A  ⫽ 70.11 mg/g  Z

346


while the degree of utilization of the bed is (eq. (4.202)) ⫽

qbr ⫽ 0.351 qo

or 35.1%. These relationships provide us with the ability to plot the utilization (%) versus Qrel (BV/h) (Figure 4.39). Low utilization means that at the breakpoint, i.e. when the operation of the bed is stopped, the portion of the solid material in the bed used during the service time is low, which is undesirable. Especially, if the material cannot be regenerated, low utilization means that the unused material is lost. Finally, as has been noted, although the exit concentration is 10 ppm, the mean concentration of the collected waste is considerably lower. The total mass of the solute passed through the bed is VbrCo, where Vbr is the exit solution volume until the breakpoint. For operation under a relative flow rate of 5 BV/h (eq. (4.200)): M out ⫽ Vbr Co ⫺ qbr Vo b ⫽ 4598 mg Then, the mean exit concentration is (eq. (4.201)) Cavr,br ⫽

M out 0.03 ppm Vbr

The mean exit concentration Cavr,br is always much lower than the breakpoint exit concentration; in our example, it is almost 0.3% of the breakpoint concentration.

100

80

η(%)

60

40

20

0 0

1

2

3

4

5

Qrel (BV/h)

Figure 4.39

Utilization (%) versus Qrel (BV/h).

6

7

4.3 Management of Spent Materials

347

4.3 MANAGEMENT OF SPENT MATERIALS 4.3.1 Activated carbon regeneration and reactivation The adsorptive capacity of carbons is obviously finite. Consequently, it is gradually decreased and finally exhausted after the adsorption of the maximum possible amount of a substance. The exhausted carbon is then characterized as “spent” and has to be regenerated, reactivated, or properly disposed off. The regeneration of spent adsorbents is the most difficult and expensive part of adsorption technology. It accounts for about 75% of total operating and maintenance cost for a fixed-bed GAC operation. Although the terms “regeneration” and “reactivation” are used interchangeably, “regeneration” means removing the contaminants from the carbon without destroying them while “reactivation” means destroying the contaminants and reactivating the carbon, which usually occurs at very high temperatures. Regeneration involves the removal of the adsorbed contaminants from carbon by means of processes that destroy neither the contaminants nor the carbon. A common process is pressure swing adsorption, where low pressure is applied to remove the contaminants from the solid phase. Other common processes involve the use of steam (volatilization of adsorbed contaminants) or the use of a hot inert gas, such as nitrogen. The contaminants are recovered as liquid after a condensation step. During the regeneration process, the contaminants are desorbed and a waste stream is produced. For instance, during steam regeneration a mixture of water and organics from the condensed desorbed vapor is produced. Thus, a regeneration process should be accompanied by a waste treatment apparatus. In most cases, the spent carbon waste to be regenerated is treated using thermal destruction/scrubbing systems. Under these conditions, the organic contaminants are destroyed and the carbon is regenerated. Spent carbon thermal reactivation involves the removal of the adsorbed contaminants from the spent activated carbon and their destruction at a high temperature (typically in excess of 800°C). Carbon losses during reactivation processes can be held at 3–15% (Zanitsch, 1997; Faust, 1987). There are several cases where regeneration or reactivation of the spent carbon is either technically or economically not viable. In these cases, the common practice is the proper disposal of the spent carbon. Generally, we proceed to the disposal if • •

the carbon is irreversibly contaminated by the adsorbed substance, the regeneration or reactivation process is too costly, mainly when dangerous substances like radioactive particles are the adsorbed species.

For example, if the carbon has been used for the removal of PCBs, dioxins, or heavy metals, and generally in the case of powdered activated carbons, the disposal of the spent material is the most appropriate method.

348


Starting with the presentation of the regeneration techniques, we could classify them into three categories (EPA, 1999): • • •

thermal swing regeneration vacuum regeneration pressure swing adsorption.

Thermal swing regeneration The spent carbon is treated with steam so that the adsorbed VOC is volatilized and forced to leave the carbon. The vapors containing the desorbed VOCs flow through another adsorber. After the desorption is complete, the treated carbon is cooled and dried by means of cooling air. Following this procedure, the spent carbons regain their initial capacity (EPA, 1999). Besides steam, other sources of heat can be also employed in the regeneration process, such as microwaves, embedded heaters, or heated nitrogen. Thermal swing regeneration is most convenient when the adsorbed species are VOCs of low vapor pressures. Vacuum regeneration If the recovery of the adsorbed VOCs is highly desirable, then instead of steam, a vacuum regeneration system may be used. According to this method, the VOCs are forced to volatilize not by temperature, but by means of pressure. Specifically, a vacuum pump is employed to decrease the pressure in the carbon below the vapor pressure of the VOCs, which leads to their boiling at ambient temperature. This method is generally used with carbons, polymers, and zeolite adsorbents (EPA, 1999). Pressure swing adsorber This system is used for the separation of gases or vapors from air (EPA, 1999). However, the concentration of the vapors has to be of the order of tens of thousands of parts per million for this technique to be applied. So, it is not suitable for the control of emissions but for removal of vapors from vents on VOC storage tanks. On exposure to a mixture of gases, the adsorbent shows a preference for some specific gases. After admitting the gas mixture, the rest of the gases plus those that are not strongly adsorbed are vented. Then, the pressure is decreased so that the remaining gases can be desorbed. This method has been used for the separation of oxygen and carbon dioxide from flue gas (EPA, 1999). 4.3.2 Ion-exchange regeneration Since ion exchange is a reversible process, most of the ion exchangers can be regenerated by means of the appropriate substance: strong acid for cation media or alkali for anion media. So, the operation life of the exchanger may be prolonged and thus money can be saved. However, the capacity of the exchanger is gradually exhausted and the medium has to be replaced after some time of operation, even with optimal regeneration. Generally, regeneration has to be conducted in different vessels, a cation and an anion vessel; otherwise the application of acidic and caustic media to the combined bed (coexistence of cation and anion beads) will lead to total deactivation of the exchange media. So, the mixed


349

bed has to be separated into the cation and the anion beads, which is usually done using hydraulic separation in situ by backwashing the bed by utilizing the small differences in the densities of the cation and anion media. After the regeneration, the media are then remixed. 4.3.3 Destruction, immobilization, and encapsulation of spent materials Spent ion-exchange and adsorption materials represent a special type of waste, and pose unique problems in the selection of their treatment options. With the evolution of environmental legislation, it is now required that spent materials meet specific quality requirements prior to disposal. In the selection of the treatment method for spent materials, their physical and chemical characteristics must be considered. Basically, there are two main treatment methods: destruction and immobilization. In several occasions, depending on the nature of the spent material, a pretreatment step is required. Pretreatment Available pretreatment processes include (IAEA, 2002) the following. Dewatering: Dewatering the spent materials prior to immobilization improves the overall volume reduction and the compatibility of the waste with the immobilization process. Dewatering is generally accomplished by pressure, vacuum filtration, or centrifugation. Drying and heating: A complete dewatering step, or drying, is sometimes required. Steam or hot air is generally used. Crushing and grinding: Crushing and grinding techniques are used basically for size reduction of spent materials, which results in a more homogeneous waste for immobilization, allows the easier removal of nonspecific materials, and facilitates the further destruction of the material by thermal, chemical, or biological methods. Destructive methods for spent materials The main goal of destructive methods is to conveniently alter the physicochemical characteristics of the spent material before its final disposal. These processes can be classified as thermal and nonthermal processes. Thermal processes Pyrolysis: Pyrolysis can be defined as the thermal degradation of organic species in the absence of oxygen or other reactant gases. However, practically, pyrolysis refers to any thermal degradation process in which less than the stoichiometric quantity of oxygen is used (Freeman, 1998). Furthermore, pyrolisis of inorganic materials is called “calcination.” Pyrolysis operates under relatively low temperatures, in the range 500–700 °C. Upon heating in the absence of oxygen, thermal cracking and condensation reactions take place converting most organics into gaseous, liquid, and solid fractions (Perry and Green, 1999). The advantages of pyrolysis are (a) (b)

the significant reduction of the waste volume, and the end product is biologically and geochemically stable (IAEA, 2002).

350


In general, pyrolysis has less environmental impact than other thermal conversion routes (Freeman, 1998). Incineration: The incineration in an oxygen-rich atmosphere results in the oxidation of the initial feed material and produces a volume reduction factor ranging from 30 to 100. Incineration systems include, among others, rotary kiln and fluidized-bed incinerators (US DOE, 1998). Ion-exchange resins contain significant amounts of sulfur and nitrogen, and thus their heating at high temperatures leads to the formation of SO2, SO3, and small amounts of NOx. Therefore, an efficient scrubbing system is required for the removal of these substances from the off-gases. Additionally, the presence of radionuclides, such as cesium and ruthenium, which are volatile at temperatures above 800 °C, makes the off-gas treatment more complex. The incineration of resins loaded with radionuclides has been used in pilot and commercial scale at the Technical Research Centre in Finland, in Japan, and at the Austrian Research Centers of Seibersdorf (Valkiainen and Nykyri, 1982; Yahata and Abe, 1982; Neubauer, 1996). Vitrification: Various molten-glass processes are commercially available for the destruction and/or immobilization of hazardous wastes (Freeman, 1998). The vitrification method destroys the combustible and some toxic portions of the waste while at the same time incorporating residuals into a glass form. Vitrification involves a heating step under extremely high temperatures, from 1100 to 3000 °C, followed by a cooling step to form a solid mass. During the cooling step, the various contaminants are trapped into the dense glassified mass formed. Plasma torches or electric arc furnaces can be used in vitrification. It is possible that some inorganic compounds will volatilize, while some organics may be destroyed during this heating step. Consequently, a system to treat these off-gases may be needed (EPA, 1996). The main advantage of vitrification is that the glass waste formed exhibits excellent leach resistance. Moreover, the ash coming from incineration or pyrolysis may be immobilized by this method (IAEA, 2002; Jantzen et al., 1995). Vitrification can be held at similar or lower temperatures than incineration, depending on the additives that have been used during glass forming. Commercial ion-exchange resin vitrification suppliers have emerged in the United States over the last few decades (Cicero and Herman, 1998; Place, 1992). Nonthermal processes There are a number of chemical treatment methods developed and used for the processing of hazardous chemical waste. These methods are briefly discussed in the next sections. Acid digestion: Sulfuric and nitric acids are applied at 250 °C to combustible waste in order to convert it into carbon dioxide and water. Although this is a very promising method for the treatment of spent ion-exchange resins, it has some serious disadvantages. The use of concentrated acids at elevated temperatures may lead to off-gases containing nitrogen and sulfur oxides that have to be treated. Moreover, very resistant materials are needed to stand the corrosion from acids, which adds to the cost of the process (Cooley and Lerch, 1974; Hawkings et al., 1980). Wet oxidation: The wet oxidation process involves the reaction of a waste with an oxidant in excess water in the presence of a catalyst (IAEA, 2002). Wet oxidation can be carried out


351

using oxygen or air as oxidant at a relatively low temperature (150–325 °C) and high pressure (2069—20,690 kPa gauge pressure) (Freeman, 1998). A more recent development is the wet oxidation process utilizing hydrogen peroxide as oxidant. The oxidation can be carried out at 100 °C under atmospheric pressure. The application of peroxide wet oxidation at low temperatures for the treatment organic waste and ion-exchange resins has been studied extensively in the United Kingdom (Wilks et al., 1991). Barix process: Barium is first added to the resin in the form of a liquid hydroxide. Subsequently, the resin is heated in the absence of oxygen and broken into its original components. Barium plays the role of catalyst in this depolymerization process and reacts with the sulfur in the cationic functional groups to form barium sulfate, which in turn acts as a binder for the metallic species in the waste. Moreover, the barium hydroxide adjusts the pH so that the metals contained in the resins stay in the residue after the steps of drying and destruction (IAEA, 2002). Direct immobilization and encapsulation of spent materials Immobilization is the process of incorporating waste into a matrix material for solidification, or directly into a storage and/or final disposal container. More specifically, solidification can be defined as encapsulation of a waste in a solid of high structural integrity (Freeman, 1998). At the same time, the goal of the solidification process is the stabilization of the waste, which means that the risk posed by the waste is reduced by converting it into a less soluble and less mobile form (Freeman, 1998). It is possible that a pretreatment step is required for organic ion-exchange materials before immobilization, although it is not definitely the case. The immobilization matrices currently used are cement, bitumen, and some polymers. Cement immobilization Another way to treat the spent materials is to immobilize them by adding cement or a cement-based mixture (EPA, 1996). A monolithic block of waste, which is very stable, is the result of this treatment. Moreover, the resulting waste forms exhibit high structural integrity, are noncombustible, and are resistant to radiation as well as to leaching (IAEA, 2002). Types of solidifying/stabilizing agents include the following: portland, gypsum, modified sulfur cement, and grout. With the exception of VOCs and pesticides, cement immobilization may be used for any kind of spent materials, especially for radioactive waste (EPA, 1996). Cement has many advantages: • • •

high availability, inexpensive raw material, conventional well-known technology.

However, there are also some disadvantages. First of all, the final waste has a very high volume compared to the initial one. Grinding before cementation may partially solve this problem. Furthermore, when organic ion-exchange materials are stored via cementation, swelling of the resin beads may occur in contact with water, which may lead to cracking of the cement. So, the cementation of such material should be preceded by an appropriate pretreatment step.

352


Bitumen immobilization/asphalt blending Bitumen (asphalt) is a generic term used to cover a wide range of high molecular weight hydrocarbons, generated in crude-oil processing. Asphalt blending has many advantages for certain types of wastes. An example is the organic wastes for which asphalt is a much better solidifying material than pozzolanic binders (Freeman, 1998). Furthermore, asphaltic binders can be used in construction works. Bitumen processes can be held either as batch or as continuous operations. In the first case, the steps of drying and mixing the dried material in molten bitumen are involved, whereas in continuous operation, the spent material is introduced as slurry to equipment that continuously mixes the bitumen at the same time. Then, the bitumen mixture flows into a suitable storage container and is solidified upon cooling. Neilson and Colombo (1982) have presented the main features of the process for ion-exchange resin wastes. Polymer immobilization The polymers used in the immobilization of spent materials can be classified into two main categories: thermoplastic and thermosetting polymers. The first type is fed in the form of a solid, and then melts upon heating and combines with the waste. On the other hand, thermosetting polymers are supplied in a liquid form and are then polymerized to a solid form, combining with the waste upon heating or in the presence of catalysts. Spent resins are generally compatible with the polymer matrix material. Generally, the polymer and the resin do not interact chemically. The immobilization of spent ion-exchange resins in polymers is a common application all over the world. Epoxy resins, polyesters, polyethylene, polystyrene and copolymers, polyurethane, phenol-formaldehyde, and polystyrene are among the polymers used (IAEA, 1988). Inorganic materials are generally not immobilized using polymers because they are more acceptable to other immobilization matrices such as cement. Properties of immobilized waste forms The properties of the immobilized waste form are decisive for the quality of disposal or storage for a long period of the spent material. Depending on the procedure followed, there is a variety of mechanical, chemical, or thermal features obtained. As mentioned earlier, cement waste forms have a very stable structure with excellent mechanical strength. However, improper formulations may lead to defects in the waste-form structure. The characteristics of bitumen waste forms are closely related to the nature of the solidifying material that has been used. The exposure of the waste form to heat or pressure may lead to its softening. Moreover, although bitumen waste forms are waterproof, rehydration of organic ion-exchange media may occur upon prolonged exposure to water and subsequent failure of the waste form due to swelling (IAEA, 2002). In many cases, the mechanical properties of a waste form are an important part of the waste acceptance criteria for storage and/or disposal of the waste. The waste form may undergo a series of tests, such as a determination of its compressive strength, to demonstrate that it will maintain its integrity over the required period of time. The resistance of the waste form to biodegradation in the environment during long-term storage or disposal is another feature of major importance. The action of microorganisms may lead to the release of the components of the waste form into the environment, or to the formation of gases such as hydrogen or methane that may be of concern for the design


353

of a repository. Since cement materials are inorganic, they are generally considered bioresistant. However, biodegradation constitutes a threat to any organic materials contained within the matrix. Alkaline conditions and the presence of biocidal additives limit the action of microorganisms. Polymers are generally very resistant to biodegradation, and thus they are considered nonbiodegradable. However, gradual degradation of many polymers may take place under the long-term effect of ultraviolet radiation. Thermal stability is another important parameter. Concerning thermal stability, there are two main considerations: the heat produced during the solidification process and the effect of exposure to heat (or cold) after the solidification of the waste form. If exothermic processes such as the polymerization of thermosetting materials are used, amounts of heat may be released high enough to deteriorate the mechanical properties of the waste. For example, at temperatures around 100 °C, the volatilization of any water contained may lead to cavities and cracks in the final waste form. After the solidification, freeze–thaw cycles may affect the properties of cement, hard bitumen, and thermoset polymer waste forms. On the other hand, the elasticity of soft bitumen and thermoplastic polymers help these materials to resist the variations of temperature. Cement waste forms are not flammable, in contrast to bitumen and polyethylene, both of which will melt producing a liquid that may maintain combustion, even after the removal of the source of the flame. Comparing thermoplastic waste forms to thermoset waste forms in terms of thermal resistance, the first type is less resistant to thermal damage. Thermoset polymers will char when contacted with a burning flame but do not maintain the flame if the source of the flame is removed. Thermal testing experiments have shown that polymethyl methacrylate matrices and polyester are adequately stable up to about 280 °C (Ghutta, 1997). An issue of great importance is the resistance of any waste form to the action of water, namely to leaching. High resistance to leaching means that the exchanged ions or the adsorbed species of concern are retained within the waste form when it is subjected to wet conditions. Leaching behavior relies on two mechanisms: (a) (b)

in the case of most polymers and bitumens, a physical barrier between the contaminant and the environment is developed, and in cement-based waste forms, the contaminant often interacts with the matrix chemically.

–5– Catalysis

5.1 TYPES OF CATALYSTS In Chapter 2, some basic definitions about catalysis were given. In this section, subjects in relation to catalysis are discussed in depth. First of all, let us examine the various types of catalysts and properties before moving on to catalysis issues. Catalysts can be sorted in various ways depending on the criterion of classification. So, they can be classified into homogeneous or heterogeneous catalysts, depending on whether they are in the same phase as reactants and products or not. Furthermore, based on its physical state, a catalyst can be gas, liquid, or solid. Depending on the substances it has been made from, a catalyst can be organic (enzymes and organic acids) or inorganic (metals, metal oxides, etc.). Finally, based on the action, catalysts can be classified as enzymatic, acid–base, photocatalytic, etc.. Here, our attention is focused on heterogeneous catalysts, solids that accelerate reactions in gas or liquid phase. In general, a solid catalyst (the whole formulation) consists of •

(a) (b) (c)

Support or carrier: The amount of the fluid adsorbed, and thus the catalytic activity, is closely related to surface area. Consequently, large surface areas are generally desired in catalytic processes and porous solids are usually employed to provide them. Moreover, supports keep the catalytic phase highly dispersed, thus protecting it from sintering. It has to be mentioned that the support may or may not be catalytically active. Using the configuration of the support as a criterion, a catalyst can be also typed as porous, when the area results mainly from the porous structure of the support, molecular sieve, when very small pores exist in the support and it is their size that decides which molecules are going to react, and monolithic, when monolith structure is used, which allows high surface area to low volume, efficient heat removal, and low pressure drop across the catalyst.

When a material used for the dispersion of the active agents is bonded to a support, it is called “washcoat.” A characteristic example is the case of automotive monolithic catalysts, where the monolith is the support and a thin film of alumina attached to the monolith constitutes the washcoat, the phase where the catalytically active metals are dispersed. In contrast to these supported catalysts, there are some catalytic materials that 355

356

5. Catalysis

can be shaped to provide large surface area, the unsupported catalysts. Activated carbon, silica gel, alumina, titanium dioxide, amorphous aluminosilicates, and zeolites are widely used as catalyst supports, alumina having a particular place in environmental applications. Synthetic zeolites have been developed and used as supports since 1960, because they have open, well-controlled framework structures that create interconnected cavities. Their structure combined with the fact that the placement of the catalytic atoms at specific sites in the zeolites is possible makes them ideal for selective catalysis. Other important perspectives of huge practical importance are connected to the so-called mesoporous catalysts, exhibiting unique properties and prepared by a revolutionary synthesis method. Rationally designed, both microporous and mesoporous inorganic catalysts are already facts for many industrial and environmentally compatible technologies. •

Catalytic agents: Mainly metals and metal oxides are used as the catalytically active components that are dispersed onto the support. The transition group elements and subgroup I are used extensively in environmental applications. Ag, Cu, Fe, Ni, their oxides, and precious metals like Pt, Pd, and Rh are a common choice in catalysis.

•

Promoters: Promoters are elements or compounds, such as cerium oxide or zirconia, used as additives to improve the physical or chemical properties of the catalysts. They can act in various ways: • • •

maintain the dispersion of the catalytic agents improve the thermal stability of the support enhance the catalytic activity

The most fundamental characteristic of a catalyst is its chemical composition, which is decisive for its specific usage (Table 5.1). The properties of a catalyst, namely activity and selectivity, have been discussed in Chapter 2. The physical properties of the catalyst are also important for its successful application. They are investigated by both adsorption methods and various instrumental techniques derived for estimating their porosity and surface area. The physical characteristics that are really important to a catalyst are surface area, particle size distribution, and particle density. These properties have been extensively discussed in Section 3.9. In Table 5.2, the surface area, pore volume, and mean pore radii are presented for some common catalysts.

5.2 BASIC PRINCIPLES OF CATALYSIS As discussed in Chapter 2, a heterogeneous catalyst accelerates a reaction that has fluidphase reactants and products, while itself remaining unchanged by the reaction. In general, it does this by stabilizing reactive intermediates whose formation would be energetically unfavorable in the fluid phase. The great advantage of heterogeneous catalysis over homogenous catalysis is that since the catalyst is in a separate phase from the reacting mixture,

5.2 Basic Principles of Catalysis

357

Table 5.1 Commercial catalysts and their uses (Gates, 1992) Catalyst

Reaction

Metals (e.g., Ni, Pd, and Pt as powders or on supports) or metal oxides (e.g. Cr2O3) Metals (e.g. Cu, Ni, Pt)

C=C bond hydrogenation, olefin + H2; paraffin C=O bond hydrogenation, acetone H2 ; isopropanol Complete oxidation of hydrocarbons, oxidation of CO 3H2 N2 ; 2NH3

Metal (e.g. Pd, Pt) Fe (supported and promoted with alkali metals) Ni Fe or Co (supported and promoted with alkali metals)

CO 3H2 ; CH4 H2O (methanation) CO H2 ; paraffins olefins H2O CO2 ( other oxygen-containing organic compounds) (Fischer–Tropsch reaction) CO 2H2 ; CH3OH

Cu (supported on ZnO, with other components, e.g. Al2O3) Re Pt (supported on n-Al2O3 or -Al2O3 promoted with chloride) Solid acids (e.g., SiO2-Al2O3, zeolites) -Al2O3 Pd supported on acidic zeolite Metal-oxide-supported complexes of Cr, Ti or Zr Metal-oxide-supported oxides of W or Re Ag (on inert support, promoted by alkali metals) V2O5 or Pt V2O5 (on metal oxide support)

Paraffin dehydrogenation, isomerization, and dehydrocyclization Paraffin cracking and isomerization Alcohol ; olefin H2O Paraffin hydrocracking Olefin polymerization, e.g. ethylene ; polyethylene Olefin metathesis, e.g. 2 propylene ; ethylene butene Ethylene ½ O2 ; ethylene oxide (with CO2 H2O) 2 SO2 O2 ; 2 SO3 Naphthalene 9/2 O2 ; phthalic anhydride 2 CO2 2 H2O Propylene ½ O2 ; acrolein CH3OH O2 ; formaldehyde (with CO2 H2O) H2O CO ; H2 CO2

Bismuth molybdate Mixed oxides of Fe and Mo Fe3O4 or metal sulfides

Table 5.2 Surface area, pore volume, and mean pore radii for typical solid catalysts (Wheeler, 1950) Catalyst

Surface area (m2/g)

Pore volume (cm3/g)

Mean pore radius (Å)

Activated carbons Silica gels SiO3–Al2O3 cracking catalysts Activated clays Activated alumina Celite (Kieselguhr) Synthetic ammonia catalysts, Fe

500∼1500 200∼600 200∼500 150∼225 175 4.2 4∼11

0.6∼0.8 0.4 0.2∼0.7 0.4∼0.52 0.39 1.1 0.12

10∼20 15∼100 33∼150 100 45 11000 200∼1000

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5. Catalysis

it can stay in the reactor as the reactants and products flow through. We will not discuss the chemistry of particular catalytic systems, but will try to develop some basic approaches that can be applied to a range of systems. The overall process of a catalytic reaction taking place in a porous catalyst particle involves the following: 1. 2. 3. 4. 5. 6. 7.

transport of reactants from the bulk of the fluid to the exterior surface of the catalyst (external mass transfer resistance), transport of reactants from the surface to the interior of the catalyst through pores (internal mass transfer resistance), adsorption of reactants onto the active sites on the internal surface of the catalyst, reaction of adsorbed reactants to form adsorbed products, desorption of products, transport of products out of the pores to the particle external surface, transport of products from the external surface of the catalyst to the main body of the fluid.

This is illustrated schematically in Figure 5.1 for the generic catalytic reaction A ; B. Steps 3, 4, and 5 are the chemically reactive steps, whereas steps 1, 2, 6, and 7, are related to transport phenomena.

stagnant film

fluid bulk

active site

A B

1

A

2

A

B 7

3 A

4

B

5

B

6

catalyst particle

Figure 5.1

Steps involved in reactions on a solid surface.


359

5.2.1 Reaction rate expressions of solid–fluid catalytic reactions The basic approach for deducing an overall rate expression of solid–fluid catalytic reactions is that each step can be considered an elementary one with its own rate law. The combination of the three rate laws (one for each step) under some assumptions leads to the derivation of the overall reaction rate expression. Before moving on, it has to be noted that catalytic reaction rate expressions can be derived in two ways: (a) the approach of Hinshelwood (1940), according to which the rates are expressed in terms of coverage , and (b) the approach of Hougen and Watson (1943), who derived rate equations in terms of surface concentrations of adsorbed species and free sites. Although at first glance these two approaches seem quite similar, the approach of Hougen and Watson proves to be more comprehensive. Their formulations provide for catalyst activity, activity decay, and catalyst effectiveness due to diffusion (Carberry, 1976). Here, the activity decay approach is followed. Adsorption The adsorption of reactants onto catalytic active sites is the first step of the “pure” catalytic process. The form of the rate law is closely connected to the mechanism adopted. Specifically, if the reactant AB is considered to be adsorbed as a molecule, it can be represented by AB S ABⴱS where: AB the reactant molecule S the catalytic active site AB*S the active site onto which a molecule of AB is adsorbed. This type of adsorption is called “molecular adsorption” in contrast to dissociative adsorption, which takes place when the molecule dissociates into atoms during adsorption and is represented by AB 2S AⴱS BⴱS How a molecule is adsorbed depends strongly on the catalyst. For example, acetaldehyde is adsorbed as a molecule on Pt, whereas dissociative adsorption takes place on the Pd catalyst (Poulopoulos et al., 2001). In the first case, the rate of attachment of AB molecules to the surface is proportional to the partial pressure of AB. Moreover, the rate is proportional to the vacant sites concentration. So, the rate of attachment is rate of attachment kA PABCV where: kA the proportionality constant for attachment, atm1 s1 PAB the partial pressure of AB, atm Cv the concentration of vacant sites, mol/gcat.

(5.1)

360

5. Catalysis

With respect to the vacant sites, the site balance can be written as Ct Cv CABⴱS

(5.2)

where: Ct the total concentration of sites Cv the concentration of vacant sites CAB*S the concentration of the sites occupied by AB. If other species are also adsorbed on the surface, the concentrations of the corresponding occupied sites have to be added to the site balance above. The concentration of the active sites is expressed as mole per unit mass of catalyst and is equal to the number of active sites per unit mass of catalyst divided by the Avogadro’s number. The rate of detachment of the molecules AB from the surface is directly proportional to the concentration of sites occupied by the molecules, for example, CAB*S: rate of detachment kA CABⴱS

(5.3)

The net rate of adsorption is equal to the attachment rate minus the detachment one. Taking into account that the ratio KA kA/k–A is the adsorption equilibrium constant, we obtain the following: rAD,m kA PABCv kA CABⴱS

(5.4)

  C rAD,m kA  PABCv ABⴱS  KA  

(5.5)

and

where rAD,m is the rate of adsorption per unit mass of catalyst, mol/(gcat s) In the case of dissociative adsorption, the adsorption rate takes another form. Since two adjacent vacant active sites are required for the molecule to adsorb, the rate of attachment is proportional to the square of the vacant sites concentration. The rate of detachment is now proportional to the product of the occupied sites concentration. Thus rAD,m kA B PABCV2 kABCAⴱSCBⴱS

(5.6)

 C C  rAD,m kAB  PABCV2 AⴱS BⴱS  K AB  

(5.7)

and


361

Surface reaction As in the case of adsorption, a variety of surface reaction mechanisms exists (Fogler, 1999) : (a)

The surface reaction follows a single-site mechanism: AⴱS BⴱS

with the following rate law:  C  rS,m kS  CAⴱS BⴱS  KS  

(5.8)

where: rS,m the rate of surface reaction per unit mass of catalyst, mol/(gcat s) KS the surface reaction equilibrium constant ks the proportionality constant, m3/(gcat s). (b)

The adsorbed reactant interacts with another site to form the product (dual site mechanism): AⴱS S BⴱS S

with the following rate law:  C C  rS,m kS  CAⴱSCv BⴱS v  KS   (c)

(5.9)

The surface reaction takes place between two adsorbed species: AⴱS BⴱSCⴱS DⴱS

and the following rate law applies:  C C  rS,m kS  CAⴱSCBⴱS CⴱS DⴱS  KS  

(d)

(5.10)

The surface reaction takes place between two species adsorbed on different types of sites S and S: AⴱS BⴱSCⴱS DⴱS

362

5. Catalysis

and the rate law now becomes  C C  rS,m kS  CAⴱSCBⴱS CⴱS DⴱS  KS  

(5.11)

All the above mechanisms, single or dual site, are known as LangmuirHinshelwood kinetics. (e)

An adsorbed molecule reacts with a molecule in the gas phase: AⴱS B(g)CⴱS D(g)

This mechanism, known as the Eley–Rideal mechanism, has the following rate law:  C P  rS,m kS  CAⴱS PB CⴱS D  KS  

(5.12)

All these expressions were derived in the same way as those for adsorption. Desorption The products of the surface reaction adsorbed are subsequently desorbed into the gas phase. The rate of desorption of C is exactly the opposite in sign to the rate of adsorption of C and the desorption equilibrium constant KDC is the reciprocal of the adsorption equilibrium constant KC. For the desorption of C, according to CⴱSC S the desorption rate law is  PC  rD,m kD  CCⴱS C v  kD 冢CCⴱS K C PCCv 冣 K DC   where: rD,m the rate of desorption per unit mass of catalyst, mol/(gcat s) kD the proportionality constant, m3/(gcat s). Derivation of the catalytic reaction rate law Suppose that the following three steps describe the catalytic reaction of A: A S AⴱS AⴱS BⴱS BⴱS B S

adsorption step surface reaction step desorption step

(5.13)


363

The rate law for each step is, respectively,  C  rAD,m kA  PA Cv AⴱS  KA  

(5.14)

 C  rS,m kS  CAⴱS BⴱS  KS  

(5.15)

 PC  rD,m kD  CBⴱS B v  K DB  

(5.16)

At steady state, the rates of the three steps are equal to one another (omitting from now on the subscript “m” for reasons of simplicity): rAD rS rD

(5.17)

We will develop an overall rate law considering that each step is rate limiting. Our aim is to eliminate the concentrations of the adsorbed species that cannot be measured during a classical experiment. Adsorption is rate limiting If the adsorption limits the overall rate, then we can assume that kS is relatively large and that rS ⬵0 kS

(5.18)

Then, the rate law of the reaction can be solved for CA*S: CAⴱS

CBⴱS KS

(5.19)

The same approximation can be applied to the rate law of desorption. We set rD ⬵0 kD

(5.20)

So, we can estimate CB*S: CBⴱS

PBCv K DB

(5.21)

364

5. Catalysis

Keep in mind that K DB

1 KB

(5.22)

Consequently, CBⴱS K B PBCv

(5.23)

Substituting CB*S into the equation of CA*S, we obtain CAⴱS

K B PBCv KS

(5.24)

The site balance is Ct Cv CAⴱS CBⴱS

(5.25)

Substituting CA*S and CB*S into the last equation, Ct Cv

K B PBCv K A PBCv KS

(5.26)

Solving for Cv, we have Cv

Ct KB 1 PB K A PB KS

(5.27)

Combining the equations for CA*S, CB*S, and Cv with the adsorption rate law, we obtain   KB PB  kA Ct  PA KS K A   rov rAD K B K A KS 1 PB KS

(5.28)

The above expression can also be written as   1 k  PA PB K S   rov rAD 1 K PB

(5.29)


365

where: k K

rov

kACt KB KAKs Ks overall reaction rate.

Surface reaction is rate limiting We will follow the same approach. If surface reaction is rate limiting, we can set rAD r ⬵ D ⬵0 kA kD

(5.30)

CAⴱS K A PA Cv

(5.31)

CBⴱS K B PBCv

(5.32)

Ct Cv CAⴱS CBⴱS

(5.33)

Then, one can easily obtain

and

The site balance is

Substituting CA*S and CB*S into the last equation, Ct Cv K A PA Cv K B PBCv

(5.34)


Ct 1 K A PA K B PB

(5.35)

Combining the equations for CA*S, CB*S, and Cv with the surface reaction rate law, we obtain   KB PB  kS K A Ct  PA KS K A   rov rAD 1 K A PA K B PB

(5.36)

366

5. Catalysis

or   KB PB k  PA K S K A   rov rAD 1 K A PA K B PB

(5.37)

where k kSKACt Desorption is rate limiting Similarly, if desorption is rate limiting, we can set r rAD ⬵ S ⬵0 kA kS

(5.38)

CAⴱS K A PA Cv

(5.39)

CBⴱS K SCAⴱS K S K A PA Cv

(5.40)

Ct Cv CAⴱS CBⴱS

(5.41)

Then, it is easily seen that

and

The site balance is

Substituting CA*S and CB*S into the last equation, we obtain Ct Cv K A PA Cv K s K A PA Cv

(5.42)


Ct 1 K A PA K S K A PA

(5.43)

Combining the equations for CA*S, CB*S, and Cv with the desorption rate law, we obtain   KB PB kD K S K A Ct  PA K S K A   rov rAD 1 K A (1 K S )PA

(5.44)


367

or   KB PB  k  PA KS K A   rov rAD 1 K PA

(5.45)

where: k kSkACt K KA (1Ks) General comments By plotting the initial overall rate with the initial pressure of reactant A, when B has not been yet produced, one can find which case is consistent with the experimental data and choose the appropriate rate form. Generally, the overall rate is expressed as the combination of three terms: rate coefficient driving force total resistance Fromment and Bishoff (1990) presented the possible expression of each of these terms in various cases, whereas Poulopoulos et al. (2001) have presented various rate laws for the catalytic oxidation of volatile organic compounds, as shown in Table 5.3. 5.2.2 External mass and heat transfer phenomena As described previously, the reactant has to first reach the external surface of the catalyst. For simplicity, we take into consideration the case of a gas reacting on a solid catalytic surface. Owing to gas film resistance, the concentration of the reactant at the catalytic surface (Cs) is lower than that in the bulk of the fluid (Cb). This difference depends on (a) (b)

the mass transfer coefficient from the fluid to the catalytic surface, and the reaction rate constant.

Consequently, the overall rate is actually lower than the one estimated based on the concentration of the reactant in the main body of the fluid. Furthermore, a temperature gradient may also be developed due to gas film resistance. This means that the temperature of the bulk of the fluid (Tb) is also different from the temperature of the exterior surface of the catalyst (Ts). As before, this difference depends on (a) (b)

the heat transfer coefficient between the fluid and the catalytic surface, and the reaction rate constant.

In addition, the temperature gradient is also dependent upon whether the reaction is exothermic or endothermic. For exothermic reactions, the catalytic surface is hotter than

368

5. Catalysis

Table 5.3 Various reaction rate laws for the catalytic oxidation of VOCs (Poulopoulos et al., 2003) Rate-limiting step

Rate Law

Scheme

km kO2 kReact CO2 CReact

Surface reaction between adsorbed oxygen and reactant

R — O2 冟冟 —S—S—

(1 kO2 CO2 kReact CReact )2

Surface reaction between dissociative adsorbed reactant and oxygen

R—O 冟冟 —S—S—

km (kO2 kReact CO2 CReact )1 2 (1 (kO2 CO2 )1 2 (kReact CReact )1 2 )2

Surface reaction between adsorbed oxygen and reactant in the gas phase


km kO2 CO2 CReact 1 kO2 CO2

Mars-van Krevelen mechanism

冟冟 R—O 冟冟 —S—S— AO2;CO2H2O

km kO2 CO2 CReact kO2 CO2 kR CReact

Surface reaction between adsorbed reactant and oxygen in the gas phase


km kReact CO2 CReact 1 kReact CReact

Catalytic surface Endothermic reaction Tb Ts

Cb

Ts

Exothermic reaction

Cs

Tb Ts fluid bulk

gas film

Heat transfer

Figure 5.2

fluid bulk

gas film

Mass transfer

Concentration and temperature profiles for fluidsolid catalytic reactions.


369

the surrounding fluid, whereas the opposite is observed in the case of endothermic reactions (Figure 5.2). In the first case, the overall rate increases as a result of the higher temperature on the catalytic surface, but at the same time decreases due to the decrease in reactant concentration. The balance between these two competitive factors can result in an overall rate higher or lower than the one corresponding to the conditions of the bulk of the fluid. In the second case (endothermic reaction), the overall rate is always lower than the rate corresponding to the conditions of the bulk of the fluid. These phenomena are called external not only because they take place outside the catalyst particle, but also because they are examined independent of the chemical reaction, in contrast to the internal mass and heat transfer phenomena. The mass and heat transfer coefficients depend on the fluid and catalytic particle properties, flow conditions, and the reactor type. Analytical equations for estimating these coefficients according to the reactor type have been presented previously in Chapter 3. Suppose that a gas reactant in a flowing fluid reacts on a nonporous catalyst at isothermal conditions. In steady state, the mass transfer rate of the reactant from the bulk of the fluid to the catalytic surface is equal to the reaction rate (nth order):

k f a s (Cb − C s ) = km C s

n

mass transfer rate

reaction rate

(5.46)

where: kf mass transfer coefficient of the reactant, m/s km reaction rate constant per unit mass of catalyst s external surface area per unit mass of catalyst, m2/kg Cb concentration of the reactant at the bulk, mol/m3 Cs concentration of the reactant at the catalytic surface, mol/m3 n reaction order. It is desirable that the overall rate rov be expressed in terms of bulk concentration, since the concentration Cb is normally determined in an experiment. Consequently, the unknown concentration Cs has to be obtained from the equation above. The concentration Cs and the overall rate rov are presented in Table 5.4 for various reaction orders, n 1, 1, ½, and 2. In the case of n 1, the overall rate can be written as

rov kovCb

(5.47)

1 1 1 kov km kf as

(5.48)

where

370

5. Catalysis

Table 5.4 Isothermal catalytic surface concentration and process rate for reaction order (n) Reaction Surface concentration order (n) (Cs)

–1

½

1

2

Overall rate (ro )

 km 1   1 1 4 k a 2  f s Cb  C Cs    b 2    

2    k  k Cs   m  Cb m  2 kf as    2 kf as   

Cs

rov

2

1 Cb k 1 m kf as

k 1 4 m Cb 1 kf as Cs k 2 m kf as

 2 3  kf as   kf as  kf as  Cs  3     2  3k m C b 2   2 km C b 2   2 km C b 

3

2 3  kf as    kf as  +3   Cb    2 km C b 2  3kmCb2    2 km C b 2  

kf as

2 km  4 km 1   1 1  Cb kf as Cb 2  

2    k  k rov km   m  Cb m  2 kf as    2 kf as   

rov

1 Cb 1 1 km kf as

  km  1 4 k a Cb 1 f s  rov km  km   2   kf as  

2

 2 3  kf as   kf as  kf as  rov km  3     2  3k m C b 2   2 km C b 2   2 km C b  3

2 3  kf as   3  kf as  3   Cb    2 km C b 2  3kmCb2    2 km C b 2  

kf as


371

and the overall rate remains first order with a constant that is the combination of the reaction and mass transfer resistance. There are two limiting cases: (a) (b)

the external mass transfer is rate limiting, where kf km and therefore rov kf asCb, and the reaction is rate limiting, where kf km and therefore rov kmCb (the system behaves as if it were homogeneous).

It is useful to introduce the external effectiveness factor ex as the ratio of the observed overall rate rov to the chemical reaction rate ro without diffusion resistance (Cs Cb): ex

rov the overall rate ro reaction rate without mass transfer

(5.49)

Moreover, the Damköhler number (Da) can be used to simplify the relations in Table 5.4: Da

km Cbn1 kf as

(5.50)

Since km Cb n-1 km Cb n-1Cb k Cn m b kf as kf s C b kf s C b

(5.51)

the Damköhler number can be seen as the ratio of the rate with reaction controlling to the rate with mass transfer controlling. A high Damköhler number means that the global rate is controlled by mass transfer phenomena. So, the process rate can be rewritten in terms of the Damköhler number and the external effectiveness factor for each reaction order can be deduced, as shown in Table 5.5. In Figure 5.3, the external effectiveness factor versus the Damköhler number is depicted for various reaction orders. It is clear that the higher the reaction order, the more obvious the external mass transfer limitation. For Damköhler numbers higher than 0.10, external mass transfer phenomena control the global rate. In the case of n 1, the external effectiveness factor actually increases with increasing the Damköhler number, i.e. as the mass transfer phenomenon prevails, because the negative order means that the increase in reactant concentration inhibits the reaction rate. It is likely that at high temperatures, where km is greatly increased, the observed rate is controlled by mass transfer, and consequently is almost the same for both catalytic and noncatalytic reactions. So far, the isothermal condition was examined. However, a temperature gradient can be developed in the gas film surrounding the catalyst pellet. The rate of heat transfer through the film is given by Q hf as (Ts Tb )

(5.52)

372

5. Catalysis

Table 5.5 The global rate expression and the external effectiveness factor for an isothermal catalytic reaction of nth order Reaction order (n)

Overall rate (rov)

–1

External effectiveness factor (ex)

2 km

2

(1 1 4 Da )Cb

1 1 4 Da

 Da 2 4 Da  1/2 km   Cb 2  

½

Da 2 4 Da 2

1

km C b 1 Da

2

 1 4 Da 1 2 km   Cb 2 Da  

 1 4 Da 1   2 Da  

 1 1 1 km  3 2  2 Da 4 Da 27 Da 3

 1 1 1 3 2  2 Da 4 Da 27 Da 3

1 1Da 2

2

3

3

1 1 1 3 2 2 Da 4 Da 27 Da 3

 3  Cb 

1 1 1 3 2 Da 4 Da 2 27 Da 3

external effectiveness factor (ηex)

10 n=-1 1 n=½ n=1 n=2 0.1 n=3

0.01 0.01

Figure 5.3

0.1

1 Da

10

100

The external effectiveness factor for reaction order n.

  

3


373

where: Q rate of heat transfer per unit mass of catalyst hf heat transfer coefficient per unit of external surface area Tb temperature at the bulk of the fluid Ts temperature at catalytic surface. The rate of heat generated by the reaction is given by q rm ( H ) km Cs ( H ) kf as (Cb Cs )( H )

(5.53)

where: q rate of heat generated per unit mass of catalyst rm global rate H heat of reaction per mole of reactant. Under steady-state conditions, the following holds: hf as (Ts Tb ) rm ( H )

(5.54)

The mass transfer coefficient can be calculated using the equations presented in Chapter 3; the heat transfer coefficient can be determined by the same or analogous equations, and thus the temperature gradient can be calculated from the reaction rate data. It has to be noted that for gas–solid catalytic reactions, the above equations can be also written in terms of pressure instead of concentration. 5.2.3 Internal mass and heat transfer phenomena As mentioned earlier, if the rate of a catalytic reaction is proportional to the surface area, then a catalyst with the highest possible area is most desirable and that is generally achieved by its porous structure. However, the reactants have to diffuse into the pores within the catalyst particle, and as a result a concentration gradient appears between the pore mouth and the interior of the catalyst. Consequently, the concentration at the exterior surface of the catalyst particle does not apply to the whole surface area and the pore diffusion limits the overall rate of reaction. The effectiveness factor s is used to account for diffusion and reaction in porous catalysts and is defined as s

actual rate of reaction (with pore diffusion resistance) rate of reaction at surface conditions

Consequently, for a first-order irreversible reaction, the observed reaction rate is rm s km Cs

(5.55)

Note that internal mass transfer and reaction are dealt with simultaneously, in contrast to external mass transfer, which is considered to be in series with the reaction at the catalyst external surface.

374

5. Catalysis

Before examining internal mass transfer and reaction, let us consider the diffusion of fluids within solid particles, which is expressed by means of the effective diffusivity. Effective diffusivity The details concerning the determination of Deff and the relevant equations have been presented in Section 3.9.9. Here, let us just recall that the effective diffusivity Deff is used to represent the diffusion of a fluid inside a catalyst particle. Deff is defined on the basis that the paths inside the particle are tortuous and that the pores have various cross-sectional areas. Moreover, not all of the area normal to the direction of the flux is available for the molecules to diffuse. The diffusion can take place by one or more of three mechanisms: •

•

•

Knudsen diffusion: This kind of diffusion is observed if the mean free path of the molecules is significantly greater than the pore diameter. In this mode, the intermolecular collisions are much less than those between the molecules and pore walls. Molecular diffusion: If the mean free path of the molecules is much smaller than the pore radius, molecular diffusion occurs and the intermolecular collisions are much more frequent than the ones between molecules and pore walls. Surface diffusion: Here, the molecules move along the walls of the pores in the direction of decreasing surface concentration. Surface diffusion is not significant in catalysis at elevated temperatures and generally is considered to be of minor importance compared to the other types of diffusion.

In Table 5.6, the porosity and the tortuosity factor are given for some catalysts. Now, our purpose is to find an expression for the calculation of the effectiveness factor accounting for reaction and mass transfer within various shapes of catalyst pellets. Plate As discussed previously, the interior surface may contribute significantly to the total one in the case of a porous catalyst. Consider the first-order irreversible reaction A; B. The reaction rate is

rm ksCA

(5.56)

where ks is the reaction rate constant per unit surface of catalyst. Table 5.6 Porosity and tortuosity factors for diffusion in catalysts Catalyst

p

p

100–110 m powder packed into a tube Pelletized Cr2O3 supported on Al2O3 Pelletized bohemite alumina Girdler G-58 Pd on alumina Haldor–Topsøe MeOH synthesis catalyst 0.5% Pd on alumina 1% Pd on alumina Pelletized Ag/8.5% Ca alloy Pelletized Ag

0.416 0.22 0.34 0.39 0.43 0.59 0.5 0.3 0.3

1.56 2.5 2.7 2.8 3.3 3.9 7.5 6.0 10.0


375

The reactant A and the product B diffuse into and out of a cylindrical catalyst pore with length L and radius rp. The material balance for reactant A at steady state for a differential length dx of the catalyst pore is written as diffusion flux in – diffusion flux out – disappearance by reaction 0    dCA     dC A  2 2  rp Deff    ksCA (2 rp x )  0    rp Deff   dx x   dx x x   

(5.57)

Dividing this equation by rp2Deffx, the following is obtained:  dCA   dC   A   dx   dx  x x x x

2 ks C A 0 Deff rp

  dC A    dC   A    dx   dx  x 2 k s C A  x x 0 lim x0   x Deff rp     

(5.58)

(5.59)

We obtain a second-order ordinary differential equation:

or

with

2 ks d 2 CA CA 0 2 Deff rp dx

(5.60)

d 2 CA 2 CA 0 dx 2

(5.61)

2 ks Deff rp

with the following boundary conditions: at the pore mouth: CA CAs, at the interior end of pore: dCA 0 , dx The general solution is

(5.62)

at x 0 at x L

CA C1e x C2 e x

(5.63)

376

5. Catalysis

CAs C1 C2 0 C1e L – C2e– L

at x 0: at x L:

C1 C2 CA ( x )

CAs e L

(5.64)

e L e L CAs e L

(5.65)

L

e e L

cosh冤 ( L x )冥 e ( Lx ) e ( Lx ) CAs CAs

L L cosh( L ) e e

or

(5.66)

x  cosh    L CA ( x ) cosh

(5.67)

where L L

km p 2 ks L Deff Deff rp

(5.68)

The dimensionless quantity φ λ L is called Thiele modulus. Finally, we obtain s

tanh

(5.69)

1 ϕ=0.5

0.9 0.8

ϕ=0.1

CA/ CAs

0.7 0.6

ϕ=0.2

0.5 0.4

ϕ=0.4

0.3 0.2

ϕ=10

0.1 0 0

0.2

0.4

0.6

0.8

1

x/L

Figure 5.4

The reactant concentration within a catalyst pore as a function of the Thiele modulus.


377

The drop in the concentration moving into the pore is shown in Figure 5.4 for various values of the Thiele modulus. Spherical pellet Following the same approach as in the case of the plate:    dCA     dC  2 2 4 (rp r )2 Deff  A   4 rp r p km CA 0 4 rp Deff       d r d r r r r     dC   dC   dC  4 rp2 Deff  A  4 rP2 Deff  A  4 2rp rDeff  A   dr  r r  dr  r  dr  r r

4 rp2 r p km CA 0

(5.70)

(5.71)

Dividing by 4 rp2Deffr:  dCA   dC    A       dr r r  dr  r  r

p km C A 2  dC A  0   rp  dr  r r Deff

(5.72)

   dCA   dC    A      dr    k C dr r  2  dCA  p m A r r  lim r0   0   Deff  r rp  dr  r r    

(5.73)

d 2CA 2 dCA p km CA 0 rp dr Deff dr 2

(5.74)

with the following boundary conditions: dCA at the center of the pellet: 0 dr at the outer surface: CA CAs

at r 0 at r rp

 r sinh  3  r rp  p CA (r ) CAs r sinh(3)

(5.75)

where

rp

km p

3

Deff

(5.76)

378

5. Catalysis

Finally, s

1 1 1    tanh(3) 3 

(5.77)

The concentration profile inside a particle is shown in Figure 5.5 for various values of the Thiele modulus. In Figure 5.6, the effectiveness factor for plate and spherical pellets is plotted against the Thiele modulus.

1 ϕ=0.3

0.9 0.8

ϕ=0.6

CA/ CAs

0.7 0.6

ϕ=1

0.5 0.4

ϕ=2

0.3 0.2

ϕ=4

0.1 0 0

0.2

0.4

0.6

0.8

1

r / rp

Figure 5.5

The reactant concentration within a catalyst pore as a function of the Thiele modulus. 1 0.9

effectiveness factor

0.8 0.7 0.6 0.5 plate

0.4 0.3

sphere

0.2 0.1 0 0

Figure 5.6

2

4 6 Thiele modulus

8

10

The effectiveness factor versus the Thiele modulus for plate and spherical pellets


379

Weisz and Prater criterion Weisz and Prater derived a criterion of strong resistance effects inside catalyst particles depending only on the concentration of the feed, catalyst measurables, and the observed rate (robs) for a first-order reaction: No mass transfer resistance inside catalyst particles (robs )L2 1 Deff CAb

(5.78)

Mass transfer resistance inside catalyst particles 2 (robs )L 1 Deff CAb

(5.79)

Example 1 The oxidation of compound A according to the reaction A ; B is to be conducted over spherical catalytic particles of radius rp 0.4 cm. The concentration distribution of A within each particle is described by the relation C(r) 4 × 104r2 mol/cm5, where r is the radial position within the particle. Given that the effective diffusivity Deff (100 )1 cm2/s, find the rate of the chemical reaction. Solution Since the concentration distribution of A within catalytic particles is known, the rate of the chemical reaction can be easily calculated as it is equal to the diffusion rate of A at r rp. Consequently,  dC  robs 4 rP2 Deff   dr  rr

p

Since  d (4104 r 2 )   dC  4 104 2 rp 3.2 104 mol cm 4    dr  rrp  dr rr p

we obtain

robs 4 0.42

1 3.2 104 2.048 106 mols 100

380

5. Catalysis

Example 2 The oxidation of a VOC to carbon dioxide is conducted over a catalytic bed consisting of porous particles with a diameter of 4 mm. The catalyst particles contain Pt as the active component, which is distributed evenly within each particle. It is suggested to use the same particles but with the Pt placed at the external surface of each particle and at a depth up to 1/10 of its diameter so that the activity of the catalyst is increased. If the reaction is first order with respect to the VOC, and on the grounds that the active surface of Pt remains the same in the two cases: (a) (b)

Estimate the change in the reaction rate. How can Pt be saved in the second case if the desired reaction rate is the same as in the first case? Given Deff 108 m2/s, initial observed reaction rate rvs 10 mol/(mcat3 s) for an initial VOC concentration 10 mol/m3.

Solution (a) Case 1: Pt is evenly distributed within catalytic particles. s

actual rate of reaction (with pore diffusion resistance) rate of reaction at surface conditions

or s

robserved 10 mol (m 3 s) $s kvs 1m 3 冢m m cat 3 s冣 3 rintrinsic kvs 10 molm

The Thiele modulus for spherical particles, and first-order reaction:

rp

km p

3

Deff

rp 3

kvs Deff

where: rp 0.002 m Deff 108 m2/s kvs the reaction rate coefficient in s1 Combining these two equations, we obtain

rp 3

r2 1 1 0 2 P 0 s2 44.4 9 s Deff s Deff

If 5, s

1


381

Substituting in the above equation, we have 44.4 (our supposition was correct) and finally, s1

1 0.022 44.4

and kvs

1 1 44.4 s1 s1 0.022

Case 2: Pt is evenly distributed within catalytic particles. Pt is placed at the external surface of each particle and at a depth up to 1/10 of its diameter. However, the reaction rate coefficient remains the same, because the surface of Pt is still the same. In this case, the only thing that changes is the volume of each particle available for the reaction. So 3

4 4  9  Vp rP3  rp  9.08109 m 3 3 3  10  A p 4 rP2 50.24 106 m 2 Then, Thiele modulus is

Vp Ap

k vs 9.08 109 Deff 50.24 106

44.4 12.04 108

Similar to the first case, we finally obtain s2 0.08 Since the reaction conditions are the same, the intrinsic rate does not change and thus robs,1 robs,2

s1 s 2

10 0.022 robs,2 36.36 mol (m 3 s) 0.08 robs,2

or in other words, robs,2 3.636robs,1 It is obvious that the modification of the catalyst led to an increase in the reaction rate by 364%.

382

5. Catalysis

(b) Since the desired rate is the same as in the first case and the reaction conditions are kept the same, the density of Pt on each particle has to be the same for both catalysts: 3

mPt ,1 Vp,1

mPt ,2 Vp,2

mPt ,2 mPt ,1

Vp,2 Vp,1

4 3 4  9  rp  rp  mPt ,2 3 3  10  0.27 4 3 mPt ,1 rp 3

or mPt ,2 0.27mPt ,1 Obviously, we saved Pt by 73% with the new formulation of the catalyst. Overall effectiveness factor It is possible to combine the resistances of internal and external mass transfer through an overall effectiveness factor, for isothermal particles and first-order reaction. Two approaches can be applied. The general idea is that the catalyst can be divided into two parts: its exterior surface and its interior surface. Therefore, the global reaction rates used here are per unit surface area of catalyst. The reaction takes place within the interior of the particle For a porous catalyst, it can be considered that there is a slight contribution of the exterior surface of the particle to the reaction. Then, the following analysis can be conducted. For the gas film,

1 dN A kf (Cb Cs ) Sex dt

(5.80)

where Sex is the external surface area of catalyst. Whereas the diffusion and the reaction (first order) inside the particle can be described by

1 dN A s kvsCs Vp dt

(5.81)

1 dN A s kvsVp dt

(5.82)

Cs

Substituting for Cs from eq. (5.82) in eq. (5.81), we obtain

1 dN A 1 Cb Sex 1 Sex dt kf s kvsVp

(5.83)


383

based on the external surface area of the particle, or

1 dN A 1 Cb Vp Vp dt 1 kf Sex s kvs

(5.84)

based on the volume of the particle. Special care should be given to units while using these expressions. In the above manipulations, kf is the mass transfer coefficient based on the exterior surface of the particle, whereas kvs is the reaction rate coefficient based on the volume of the particle. If we define an overall effectiveness factor as

ov

actual overall rate rate that would result if the entire surface were exposed to the bulk concentration C b

(5.85)

So, for a first-order reaction,

ov

actual overall rate kvsCb

(5.86)

which, for the specific case, results in

nov 1

s s kvsVp

(5.87)

kf Sex

The reaction takes place both on the exterior and within the interior of the particle When the reaction at the exterior surface area of the catalyst cannot be overlooked, then the following manipulation has to be conducted to determine the reaction rate (Levenspiel, 1972). This is the case when the reaction is so fast that the reactant has little chance to diffuse into the particle. For the gas film,

kS 1 f ex (Cb Cs ) Vp Vp

(5.88)

384

5. Catalysis

whereas the diffusion and the reaction inside the particle can be described from

Sex Sin 1 dN A Cs kvs Cs s kvs Vp dt Sin Sex Sin Sex

(5.89)

where Sin is solely the internal surface area of the particle. Note that if Sin Sex then we obtain the eq. (5.81). Finally,

1 dN A 1 Cb Vp Vp dt 1 kf Sex k  Sin  k  Sex  s vs  vs  (Sin Sex ) (Sin Sex )

(5.90)

So, the overall effectiveness factor ov is ov

1 kvsVp kf Sex

1 S S s  in (S S ) ex (S S )   in ex in ex

(5.91)

and the overall rate can be written as rov ov kvsCb

(5.92)

5.3 DESIGN OF CATALYTIC REACTORS In Chapter 3, the reactor models have been presented along with the hydraulic and mass/heat transfer analysis. In the following sections, the solutions of the reactor models are presented along with several examples. 5.3.1 Two-phase agitated reactors Batch reactors The integrated form of the material balance for the case of a constant-volume system becomes (Levenspiel, 1972) x

C

o dx dC ∫ ( ) ( r ru ) u C 0

t Ci ∫

i

(5.93)

5.3 Design of Catalytic Reactors

385

The integrated form of the material balance for the case of a constant-volume system can be expressed in terms of several units (Levenspiel, 1972): x

x

x

x

x

V V V V t dx dx dx dx dx ∫ L∫ L∫ L∫ L∫ Ci 0 (ru ) MS 0 (rm ) S 0 (rs ) VS 0 (rvs ) VR 0 (R)

(5.94)

Note that all rates denote the rate of disappearing of the reactant. For example, for a zeroorder reaction (for t < Ci/k), Ci xA kt

(5.95)

ln(1 xA ) kt

(5.96)

and for a first-order reaction,

The integrated form of the BCST material balance for the case of a variable-volume system becomes (Levenspiel, 1972) xA

t N A,i

∫ 0

dxA CA,i (ru )V

xA

∫ 0

dx A (ru )(1 R xA )

(5.97)

For example, for a zero-order reaction and a variable-volume system, Ci ln 冢1 R xA 冣 kt R

(5.98)

 V  ln(1 xA ) ln  1 kt RVi  

(5.99)

and for a first-order reaction,

Comparing this result with that of a constant-volume system, we see that the fractional conversion at any time is the same in both cases. However, the concentration of materials is not the same. Continuous flow reactors The material balance is Fi xA (ru ) VL

(5.100)

386

5. Catalysis

or VL x A Fi Ci (ru )

(5.101)

For a constant-volume system, xA 1

Co Ci

(5.102)

and thus VL Ci Co Fi Ci (ru )

(5.103)

For example, for a first-order reaction, xA 1 xA

(5.104)

xA 冢1 R xA 冣 1 xA

(5.105)

k

and for a variable-volume system,

k

5.3.2 Slurry bubble column reactors In the following sections, the solutions of the models as well as various examples will be presented for the case of slurry bubble column reactors. Gas-phase reactions and batch liquid Consider the reaction aA (g) B (g) → products For a first-order reaction with respect to A, the rate form is (r ) k C m m A,s

(5.106)


387

By using the appropriate equations and following the procedure illustrated in Section 3.4.6 for the derivation of the overall rate, we obtain (ru )A K CAG,z

(5.107)

 1 1 1 1  ⬵ HA   K

 kfg aGL kf ac s ms km 

(5.108)

(ru )A a(ru )B

(5.109)

where

Furthermore,

The equations to be solved are CAG,i CBG,i

xo

dx

∫ (ruA)A

(5.110)

xi

xo

dx

∫ (ruB)B

(5.111)

xi

For sizing the reactor, the equation for the limiting reactant should be used. For A as the limiting reactant, the corresponding equation becomes CAG,i

xo

∫

xi

x

o dxA dx A ∫ K CAG,z x K CAG,i (1 xA )

(5.112)

i

or xo

K

dx

∫ 1 xAA

(5.113)

xi

and the solution for constant-density systems (R 0) is (Smith, 1981)  aCBG,i  xA  K ln  1 CAG,i  

(5.114)

388

5. Catalysis

or xA 1 exp(K )

(5.115)

Note that aGL, ac, ms, and are based on the unit volume of bubble-free liquid VL. The equation remains exactly the same, even if these parameters are expressed per unit volume of reactor VR. The conversion of B can be evaluated by using the stoichiometry of the reaction. It is easy to show that if A is the limiting reactant,

CBG,z CBG,i xA

CAG,i

(5.116)

a

or

xB xA

CAG,i

(5.117)

aCBG,i

In the case of A as limiting reactant and a variable-density system, the solution of the model is the same as for first-order irreversible homogeneous reactions of the form A ;products (Levenspiel, 1972): K (1 R )ln (1 xA ) R xA

(5.118)

For B as the limiting reactant, we have CBG,i

xo

∫

xi

x

x

o o dx B adxB adxB ∫ ∫ (ru )B x (ru )A x K CAG,z i

(5.119)

i

Note that the gas phase flows under plug-flow condition, and thus the concentrations of both reactants change with position (CG,z). For a constant gas-phase concentration of A, the reaction rate is constant and the reaction term can be drawn out of the integration and the above equation becomes (Smith, 1981)

xB

K CAG,i aCBG,i

This is the case when A is in great excess in the gas mixture.

(5.120)


389

For a varying gas-phase concentration of A, the exit concentration of A can be evaluated using the stoichiometry of the reaction. It is easy to show that if B is the limiting reactant at each axial position z in the reactor, CAG,z CAG,i axBCBG,i

(5.121)

Then x

o dxB K

1 ∫ aCBG,i x CAG,i axBCBG,i CAG,i i

xo

dxB CBG,i xi 1 ax B CAG,i

∫

(5.122)

For R 0, the solution is K

1 aCBG,i CAG,i

 CAG,i  aCBG,i   xB   ln  1  CAG,i    aCBG,i 

(5.123)

Thus  aCBG,i  xB  K ln  1 CAG,i  

(5.124)

or xB

CAG,i aCBG,i

冤1 exp(K )冥

(5.125)

Here, note that xB

aCBG,i CAG,i

xA

(5.126)

as expected. Although the solutions irrespective of the limiting reactant are the same, it has to be pointed out that the sizing of the reactor is based on the limiting reactant or, in other words, on the conversion of the limiting reactant. Example 3 Consider the gas-phase reaction AB C

390

5. Catalysis

If the feed is equimolar, derive the reactor model and express it as a function of the total pressure. Solution If a 1, R 0 and the feed is considered to be equimolar, the number of moles of each component at a conversion level of x is the following: A FA (1 x), B FA (1 x), C FAx, and the total number of moles FA (2 x). At inlet conditions x 0, CG,i

1 Ptotal 2 RT

The parameter R is R

FA 2 FA 1 2 FA 2

Then 1 K 冤 x ln(1 x )冥 2 Finally, V 1 RT L 冤 x ln(1 x )冥 CG,i FA K Ptotal

Example 4 The removal of sulfur dioxide from air has been studied by Komiyama and Smith (1975) in an agitated slurry reactor using activated carbon particles as catalyst at 25 °C and 1 atm. The gas stream consisted of 2.3% SO2 and 21% oxygen, and pure water was used as liquid phase. Activated carbon catalyses the oxidation of SO2 to SO3, which dissolves in the water to produce H2SO4: SO2 (g) 12 O2 (g) H 2 O(l)H 2 SO 4 (aq) The intrinsic rate of the reaction is controlled by the rate of adsorption of oxygen on the carbon and is independent of the SO2 concentration. The following data have been given by Komiyama and Smith: particle size 0.03 mm, particle density 800 kg/m3, effectiveness factor 0.861, and kvs 6.6 m3/m3s.


391

Suppose that the reaction takes place in a slurry bubble column and that it is desirable to work in the heterogeneous flow regime. Moreover, assume that the liquid is batch and well mixed and the flow of the gas approximates the ideal plug flow. Komiyama and Smith have studied the reaction using He as the carrier gas. However, for the purposes of the example, suppose that the intrinsic reaction constant is not affected by the carrier gas, which is the nitrogen in our case. This is reasonable as both gases are inert. Assume also zero expansion. Design a slurry bubble column reactor and estimate its performance for catalyst loading of 20 kg/m3 of reactor (2% catalyst loading). Solution The first step in the solution of the example is the design of the reactor, which will determine its hydraulic parameters and thus the mass transfer coefficients, which are an input in the model of the reactor. Hydraulic analysis: Using Figure 3.27, the appropriate gas superficial velocity and the column diameter for the heterogeneous flow regime can be selected. An appropriate choice for the reactor diameter and the superficial gas velocity is 0.5 m and 0.1 m/s, respectively. The height to diameter ratio in columns is greater than unity and a value of 5 is reasonable. Therefore, the value of 2.5 m has been selected for the column height. As a result, the reactor volume is equal to 0.49 m3. This volume is occupied by the reaction mixture, which is the gas, the liquid, and the solid phase. For the specific catalyst loading per reactor volume ms,tot, the mass of catalyst is Ms ms,tot VR 9.81kg and its volume is Vs

Ms 0.012 m 3 p

The gas volume can be determined by the gas holdup. Using the Akita–Yoshida correlation (3.161) and using a trial-and-error procedure, the gas holdup is hG 0.17 (n 0.2). The gas volume is

VG hGVR 0.081m 3 The use of the Akita–Yoshida correlation is justified since the catalyst loading is very low (2%), and the effect of the solids on gas holdup is expected to be minimal. The liquid volume is simply VL VR Vs VG 0.397 m 3

392

5. Catalysis

and the liquid holdup is (eq. (3.169)) hL

VL 0.81 VR

As has been analyzed, the basic model for bubble column assumes complete mixed flow for the liquid phase and plug flow for the gas phase. The Deckwer et al. correlation (3.202) for the liquid phase and the Field and Davidson equation (3.206) for the gas phase can be used for the estimation of the dispersion coefficient. The resulting coefficients are DLL 0.09 m2/s and DLG 0.49 m2/s. Concerning the liquid phase, the dispersion coefficient is well above the value of 0.01 m2/s for liquids, which is considered to be indicative of good mixing conditions. Although the gas dispersion coefficient is much higher, this does not mean that the mixing in this phase is higher. By analogy to fixed beds, the degree of mixing depends on the dispersion coefficient as well as the velocity of the flowing phase. For plug flow, we need a low dispersion coefficient and a high fluid velocity. In our case, the liquid is batch, while the gas flows with a superficial velocity of 10 cm/s. Here, it has to be noted that for calculating the Peclet number in fixed beds, the actual velocity has to be used, i.e. the interstitial velocity, which influences the degree of mixing. In slurry bubble column reactors, the real velocity of the fluid is the bubble velocity, which is much higher than the gas superficial velocity. The mean bubble rise velocity for a batch liquid is (eq (3.201)) ubub

usG 60 cms hG

By using Fukuyama correlation (3.188), the bubble diameter is 2

0.59   0.0153m dbub g  hG  where, for a batch liquid (eq. (3.189)), usG (1 hG ) 0.083 Using the correlation of Mendelson (3.191), the single bubble rising velocity is calculated as 29 cm/s. Mass transfer coefficients: The liquid–solid mass transfer coefficient can be evaluated by using the correlation of Sano et al. (eq. (3.211)): kf 4.22 × 104 m/s, whereas the corresponding interfacial area is (eq. (3.218)) ac

6 ms dp p


393

Note that the ms,tot is known, i.e. the catalyst loading per unit volume of reactor. Its value per unit volume of liquid is ms

ms,tot hL

20 24.70 kgm 3 0.81

and thus, ac 6175 m2/m3 liquid. The gas–liquid mass transfer coefficient is calculated using the correlation of Yasunishi (eq. (3.223)), with s

Vs 0.012 0.03 VL Vs 0.397 0.012

calculated by using eq. (3.172). Then, kfgaGL,tot 0.10 s1. This coefficient can also be expressed per unit volume of liquid by using eq. (3.230): kfg aGL

kfg aGL,tot hL

0.10 0.13s1 0.81

Reactor model: The overall coefficient Ko is (eq. (3.144))  1 1 1 1  ⬵ HA   K

 kfg aGL kf ac s ms km  where for the specific temperature HA 32.07 (Annex I, Section I.3.2). Furthermore, from eq. (3.10), km

kvs 8.25 103 m 3 kg s p

then Ko 2.25 × 103 s1. One more input is needed for the model, which is the inlet concentration of the reactants. By using the ideal gas law, the concentration of oxygen is found to be 8.59 × 106 mol/cm3 and that of sulfur dioxide 9.41 × 107 mol/cm3. The limiting reactant is SO2 and the expansion factor is zero. Then using eq. (5.125), xB

CAG,i aCBG,i

冤1 exp(K )冥 0.81

or 81% (A: O2, B: SO2, and a 0.5). Furthermore, from eq. (5.126), xA xB

aCBG,i CAG,i

0.04

394

5. Catalysis

or 4%. Assuming constant oxygen concentration and using the simplified model (eq. (5.120)) we obtain xB

K CAG,i aCBG,i

0.83

which is somewhat higher than the actual conversion, as expected. A few comments: Sulfur dioxide (SO2) is a gas produced by volcanoes and from many industrial processes. It is sometimes used as a preservative in alcoholic drinks, or dried apricots and other fruits. Generally, the combustion of fossil fuels containing sulfur compounds such as coal and petroleum results in sulfur dioxide being emitted into the atmosphere. Beyond its irritating effect on the lungs, sulfur dioxide is also a threat to the environment, since it is well known to contribute to acid-rain formation. Gas–liquid reactions and batch liquid The following analysis is valid for • •

slurry bubble bed column and agitated slurry reactor under the same conditions, packed bubble bed reactor or trickle-bed reactor under complete recycling of the liquid stream.

In the analysis given below, the following form or reaction is considered: aA(g) B(l) products Constant gas-phase concentration and first-order reaction in respect to gas reactant By using the appropriate equations and following the procedure illustrated in Section 3.4.6, for a first-order reaction with respect to A, we have (ru )A K CAG

(5.127)

 1 1 1 1  ⬵ HA   K


(5.128)

where

The equation to be solved is (ru )B

(ru )A dx CBL,i B a dt

(5.129)


395

or K CAG dx CBL,i B dt a

(5.130)

The solution is (Ramachandran and Chaudhari, 1980)

t

aCBL,i xB K CAG

aCBL,i xB  1 1 1     CAG   kfg aGL kf ac s ms km  HA  

(5.131)

or in another useful form, xB

K CAG t aCBL,i

(5.132)

Note that aGL, ac, and ms are based on the unit volume of bubble-free liquid VL. If the parameters per unit volume of reactor VR are used in this equation instead, then the righthand side of eq. (5.131) should be multiplied with the liquid holdup hL, or in terms of liquid and reactor volume,

t

 VL aCBL,i xB  1 1 1   VR  CAG   kfg aGL,tot kf ac,tot s ms,tot km  HA  

(5.133)

where the parameters in the parenthesis are based on the reactor volume. If the liquid phase is saturated with the gas reactant, (ru ) B

(ru )A dx CBL,i B a dt

(5.134)

or k CAG dx CBL,i B dt aH A

(5.135)

and the solution is t

aCBL,i H A xB k CAG

aCBL,i xB  1 1    CAG   kf ac s ms km  HA  

(5.136)

396

5. Catalysis

Based on the reactor volume,

t

 VL aCBL,i xB  1 1  VR  CAG   kf ac,tot s ms,tot km  H  A

(5.137)

or in another useful form,

xB

k CAG t aH A CBL,i

(5.138)

Note that in contrast to the Ramachandran and Chaudhari solution, the resistance of the gas–liquid interface is missing. This was expected as the liquid is saturated with the gas reactant. For the special case of variable gas-phase concentration and zero expansion, the solution (eq. (5.131)) becomes (Ramachandran and Chaudhari, 1980) t

aCBL,i xB K CAG

aCBL,i xB  1 1 1    CAG,i   kf ac s ms km   H A 

(5.139)

where

H A QG VR

  K L aGL Z   1 exp    usG H A   

(5.140)

Note that the initial gas-phase concentration CAG,i is found in the solution. In the case of constant gas-phase concentration or if the gas is sparingly soluble even if its concentration is variable,

kfg aGL

(5.141)

and the solution becomes identical to eq. (5.131). Constant gas-phase concentration and first-order reaction in respect to liquid reactant By using the appropriate equations and following the procedure illustrated in Section 3.4.6, for a first-order reaction with respect to B, (ru )B (kf ac ) B 冤CBL CBS 冥 ms s km CBS

(5.142)


397

or (kf ac )B CBL (kf ac )B ms s km

(5.143)

(kf ac )B CBL k CBL (kf ac )B ms s km

(5.144)

(kf ac )B ms s km 1 1 1 (kf ac )B ms s km k (kf ac )B ms s km

(5.145)

CBS and thus (ru )B ms s km where k

The equation to be solved is (ru )B

dCB dt

(5.146)

k CBL

dC B dt

(5.147)

or

The solution is CBL exp(k t ) CBL,i

(5.148)

In terms of fractional removal of B: xB

CBL,i CBL CBL,i

1 exp(k t )

(5.149)

or

t

1  1   1 1   1   ln  ln  k  1 xB   (kf ac )B ms s km   1 xB 

(5.150)

398

5. Catalysis

Based on the reactor volume: t

VL 1  1  ln VR k  1 xB 

(5.151)

Note that the parameter VL/VR in a slurry reactor is the fractional liquid holdup. In the packed bubble bed reactor and the trickle-bed reactor, under complete recycling of the liquid stream, VL/VR is the ratio of total volume of the liquid that is processed (recycled) to the volume of reactor, and is always greater than 1. By recycling, it is possible to process a larger volume of liquid than the reactor volume by having a surge tank in the recycle line. Finally, note that in the solution (5.151) all the involved reactor parameters should be based on the reactor volume. 5.3.3 Agitated slurry reactors In the following sections, the solutions of the models as well as examples will be presented for the case of slurry agitated reactors. Gas–liquid reactions and continuous flow of both phases The following analysis is valid for • • •

Slurry agitated reactor for continuous flow of both phases (CSTR conditions) Slurry bubble bed column under compete mixing of both phases Packed bubble bed reactor under complete mixing of both phases.

General solution The overall catalytic reaction is aA (gas) B(liquid)products For a first-order reaction with respect to A, (ru )A (kf ac ) A CAL,o CAS  ms s km CAS

(5.152)

or CAS

(kf ac )A CAL,o (kf ac )A ms s km

(5.153)

(k f a c )A CAL,o k CAL,o (kf ac )A ms s km

(5.154)

and thus (ru )A ms s km


399

where k

(kf ac )A ms s km 1 1 1 (kf ac ) A ms s km k (kf ac )A ms s km

(5.155)

The solution of the model is

CAG,o CAG,i

( K L GL ) VL H A QG

CAL,o

QL ( K L GL ) k VL QL ( K L GL ) k VL  QL   V k 

(5.157)

L

( K L GL ) QL CAL,i CAG,i VL

CAG,o  QL   V  CAL,i H 冢 K L GL 冣 A L  QL   V  冢 K L GL 冣 k

L

CBL,o

(5.156)

 k oCAL,o   QL  C BL,i  a   V    L  QL   V  L

(5.158)

(5.159)

(5.160)

Note that aGL, ac, ms, and Qi/Vi are based on the unit volume of bubble-free liquid VL. If parameters per unit volume of reactor VR are used instead in the above equations, then the term should be divided by the liquid holdup hL. Constant gas-phase concentration Under the limiting condition of constant gas-phase concentration and for a first-order reaction with respect to A, the general solution is reduced to (Ramachandran and Chaudhari, 1980)

CAL,o

CAG,o  QL   V  CAL,i H ( K L GL ) A L  QL   V  ( K L GL ) k

L

(5.161)

400

5. Catalysis

CBL,o

 QL   k CAL,o   V  CBL,i  a  L  QL   V  L

(5.162)

where k

(kf ac )A ms s km 1 1 1 (kf ac )A ms s km k (kf ac )A ms s km

(5.163)

For a first-order reaction with respect to B, we have QL 冢CBL,i CBL,o 冣冢 kf ac 冣 B 冢CBL,o CBS 冣 0 VL

(5.164)

Component mass balance around the catalyst is 冢 kf ac 冣 B 冤CBL,o CBS 冥 (ru )B ms rm ms km CBS

(5.165)

(kf ac )B ms s km 1 1 1 (kf ac )B ms s km k (kf ac )B ms s km

(5.166)

Then k

The solution is (Ramachandran and Chaudhari, 1984) CBL,o

or xB

CBL,i V  k  L  1  QL 

(5.167)

V  k  L   QL  V  k  L  1  QL 

(5.168)

Note that aGL, ac, ms, and Qi/Vi are based on the unit volume of bubble-free liquid VL. The equation remains exactly the same if parameters per unit volume of reactor VR are used instead in the above solution.


401

Example 5 The catalytic oxidation of ethanol is catalyzed by Pd–Al2O3 at 30 °C, and is considered to be of first order with respect to oxygen (Hopper et al., 2001; Ramachandran and Chaudhari, 1980). The rate constant for this reaction is km 0.0177 cm3/(g s). The complete oxidation of ethanol is represented by the following reaction: C2 H 5 OH(l) 3O2 (g)2CO2 (g) 3H 2 O However, Hopper et al. (2001) as well as Ramachandran and Chaudhari (1980) considered that the stoichiometric coefficient of oxygen is unity, and thus the oxidation of ethanol can be considered to be as follows: C2 H 5 OH(l) O2 (g)2C(s) 3H 2 O Estimate the conversion of ethanol in a CSTR with continuous flow of slurry and gas and rigorous agitation. The volume of the tank is 69.78 L. Suppose that a flat-blade disk turbine with six blades is used and the agitation rate is 800 rpm. For the other characteristics of the tank assume a typical design configuration. The following data are given: gas flow rate 3140 cm3/s, liquid flow rate 62.8 cm3/s, ethanol inlet concentration 4 × 104 mol/cm3, oxygen pressure 35.5 atm, molecular diffusivity of oxygen in water 4.7 × 105 cm2/s, catalyst load ms 0.1 g/cm3liquid, effectiveness factor 0.8, catalyst particle size 0.05 cm, and particle density 1200 kg/m3. The inlet oxygen concentration in the liquid phase is zero. Under the specified conditions, assume that the expansion of the gas phase is zero. Solution The first step in the solution of the example is the design of the agitated tank, which will determine its hydraulic parameters and thus the mass transfer coefficients, which are an input in the model of the reactor. The volume of the tank is 69.78 L. However, for safety reasons (e.g. overflow due to vortex formation), we should use a part of this volume for the reaction mixture, lets say 90%, i.e. 62.8 L. This volume is what we call “reactor volume” when analyzing slurries, i.e. it is the reaction volume, which is the sum of the liquid, gas, and solids volume. Hydraulic analysis: Thus, we suppose a typical design for the agitated tank which means that (Table 3.3) DT H L and Da

DT 3

402

5. Catalysis

For this calculation, we use the height of the liquid in liquid–solid systems. However, in three-phase systems we should use the height of the slurry, and thus the volume of the reactor. Note that the reactor volume is the sum of the liquid, gas, and solids volume. The tank diameter can be calculated from the reactor volume (eq. (3.113)):  4V  DT  R   

1 3

0.43m

Then, the impeller diameter is Da 0.14 m. Having the tank dimensions and the rotational speed, we can evaluate the Reynolds number (eq. (3.104)): N Re

Da2 N 3.06 105

and thus the power number NP of the given impeller is 5.75 (Table 3.4). The power needed is (eq. (3.107)) P N P N 3 Da5 833.09 W and the energy of dissipation per unit mass of liquid can be evaluated from eq. (3.256): o

P ML

At this point, we need the mass of liquid ML and thus its volume VL, which is not known. So, we have to assume a value of its volume or in other words we have to assume a value for the fractional liquid holdup hL. Then, VL hLVR where VR is the reactor volume, which is known. Because we have gas injection in the liquid, the energy of dissipation per unit mass of liquid in a fully baffled agitated system in turbulent flow is given by eq. (3.257): o where is a correction factor (eq. (3.259)): QG NDa3

7.95102 3.5102

0.62 1.85

QG NDa3

0.47


403

This correlation has been suggested for gas–liquid systems. In the presence of solids, in three-phase systems, the energy of dissipation can be approximately 25–50% higher depending on the loading and density of particles (Ramachandran and Chaudhari, 1984). Here, we use the average value of 37.5% and thus 1.375 o Having all these parameters of the system, we can evaluate the gas holdup hG by using the Calderbank equation (3.260). At this point, we have to check if the assumed fractional liquid holdup corresponds to the real one, which is

hL

VL VR

where VL VR VG VS The volume of gas is VG hGVR while the volume of solids is

VS

msVL p

It is obvious that we need a trial-and-error procedure. A good approximation for slurry reactors is a fractional liquid holdup of 85%. This value can be used to initiate the iteration procedure. At first we assume a value of the liquid fractional holdup hL, and thus the values of liquid volume VL, mass volume ML, and solids volume VS are known. Then, we can evaluate the parameters o and 苶. Note that at this point, the procedure is complicated due to one more trial-and-error procedure associated with the evaluation of gas fractional holdup hG (eq. (3.260)). After the evaluation of hG, the gas volume VG is known. Then we can re-evaluate the liquid volume (VL VR VG VS) and compare it with the assumed value. The iteration continues until these two values are the same. The trial-and-error results are given in Table 5.7. In the above analysis, we used Calderbank’s equation for the determination of gas holdup. This correlation has been derived for gas–liquid systems. However, as in the case of slurry bubble columns, the presence of solids is not expected to alter the gas holdup significantly,

404

5. Catalysis

Table 5.7 Trial-and-error procedure results Parameter

Value

ε苶 (m2/s3) hG (m3/m3) VG (m3) VS (m3) VL (m3) hL (m3/m3)

11.21 0.17 10.44 4.03 48.33 0.77

at least for such a low solid loading (6.5% of the total reactor volume). The same holds for the effect of liquid flow, as its flow rate is low (2% of the gas flow rate). Mass transfer coefficients: The liquid–solid mass transfer coefficient can be evaluated by means of the correlation of Sano et al. (eq. (3.266)):  dp4 3L  2 0.4  3  Df  L 

kf dp

0.25

 L   D  L f

1 3

kf 6.97104 ms

and the gas–liquid mass transfer coefficient is (eq. (3.268))   kfg 0.592 Dfg0.5    vL 

0.25

2.41103 ms

The corresponding interfaces per unit volume of reactor are (eq. (3.219))

ac,tot

6 ms hL 769.64 m 2 m 3 dp p

and from eq. (3.229), aGL,tot

6 dbub

hG 965.79 m 2 m 3

where the bubble diameter has been evaluated to be 1.01 mm by using the Calderbank equation (3.263). Reactor model: Since we have at hand the mass transfer per volume of reactor, we can use the model equations as presented in the previous paragraphs by expressing the involved


405

parameters per unit volume of reactor and using the volume of the reactor instead of the volume of the liquid. The only modification is that the parameter should be divided by the liquid hold up hL. To determine the coefficient ko, the catalyst loading per unit volume of reactor is needed, which is ms,tot ms hL 76.96 kgm 3 Then, from eq. (5.155), ko 1.09 103 s1. One more input is needed for the model, which is the dimensionless Henry’s constant for oxygen at 30 °C. Henry’s constant is estimated by using the IAPWS correlation (see Section I.3.2 in Appendix I ) and is H 34.03. Then, by using model equations (5.156)–(5.160), we have 2.66 103 and 0 and thus the conversion of ethanol is found to be equal to 10.8%. The exit concentrations in both phases are shown in Table 5.8. Table 5.8 The exit concentrations Gas-phase concentration of oxygen (CAG,o, mol/m3) Liquid-phase concentration of oxygen (CAL,o, mol/m3) Liquid-phase concentration of ethanol (CBL,o, mol/m3)

1427.05 41.90 374.43

A few comments: Beyond water, ethanol is perhaps the most commonly used solvent in chemistry. It has also been used extensively in some countries as fuel. As a result, its presence in the environment, aquatic and atmospheric, is increased. Ethanol in great doses has been recognized as a human teratogen, well before experimental studies in animals were undertaken. However, the consumption of alcohol via drinks and beverages covers any adverse effects due to ethanol’s presence in the environment.

Gas–liquid reactions and batch liquid In this case, the solutions derived for the slurry bubble column reactor are applicable. Gas-phase reactions and batch liquid

Consider the reaction

aA(g) B(g)products Using the appropriate equations and following the procedure illustrated in Section 3.4.6 for first-order reaction with respect to A, we have (ru )A K CAG,o

(5.169)

406

5. Catalysis

where  1 1 1 1  ⬵ HA   K


(5.170)

The equations to be solved are xA CAG,i (ru )A

(5.171)

x axB B CB,i (ru )B (ru )A

(5.172)

The solution of the model for a first-order irreversible reaction is the same as that of homogeneous reactions (R 0) (Levenspiel, 1972): xA K CG,i CG,i (1 xA )

(5.173)

or K 1 K

(5.174)

K CAG,o a CBG,i

(5.175)

CAG,i K CAG,i xA 1 K aCBG,i aCBG,i

(5.176)

xA and xB

or xB

If A or B is the limiting reactant, then by setting xA 1 or xB 1 the maximum residence time needed for complete conversion can be evaluated. This value can be used to determine the maximum conversion of the nonlimiting reactant. In any case, the equation of the limiting reactant is used for sizing the reactor. If the gas-phase concentration of A is constant,


407

xB

K CAG,i a CBG,i

(5.177)

If A is the limiting reactant and for the general case of R ≠ 0, the solution is x (1 R xA ) K A CAG,i CAG,i (1 xA )

(5.178)

Note that aGL, ac, ms, and are based on the unit volume of bubble-free liquid VL. The equation remains exactly the same if parameters per unit volume of reactor VR are used instead in the above solution. 5.3.4 Fixed-bed reactor The basic equations that describe fixed-bed reactors have been presented in Section 3.6.2. In the present Section Isothermal, Adiabatic and Non-isobaric fixed bed operations as well as the case of Monolithic catalysts are presented. It is common for the volume of reactor VR to be replaced by the catalyst weight W in catalytic reactors. The surface area of the catalyst could also be used, but since it is much harder to determine than its weight, it is common in industry to give catalytic reaction rates per catalyst weight. Keep in mind that VR

W b

(5.179)

where b is the bulk density of the catalyst. For constant superficial velocity (zero expansion factor) and negligible pressure drop (see the subsection Nonisobaric fixed-bed operation), the general mass and thermal energy continuity equations for the catalytic fixed-bed reactor are

cp

 2 C 1 C  C 2 C C DL 2 DR  2 us b (rm )  t z r r z  r 

(5.180)

 2 T 1 T  T 4 h

2 T T L 2 R  2 cp us (T Tw ) H b (rm )  D r r z t z   r (5.181)

Nowadays, high computer capacities and sophisticated programs provide us with the capability to design reactors and simulate their operation managing all the complexity of the equations. However, the presentation of numerical solutions of complex equation systems is beyond the scope of this book. Various simplifications, not far from reality, will

408

5. Catalysis

allow us to connect the conversion with the various reactor design parameters such as catalyst weight, reactor dimensions, and flow conditions. At steady-state operation ( ∂C/t ∂T/∂t 0), the conservation equations presented above simplify to DL

L

 2 C 1 C  2 C C us b (rm ) D R z z 2  r 2 r r 

 2 T 1 T  2 T T 4 h

(T Tw ) H b (rm ) c p us

R   2 2 D r r  z z  r

(5.182)

(5.183)

In addition, if no radial dispersion takes place, these equations further simplify to 2 C C us b (rm ) 2 z z

(5.184)

2 T T 4 h

(T Tw ) H b (rm ) cp us 2 D z z

(5.185)

D

L

Finally, for the ideal plug-flow reactor, the equations reduce to us cp us

C b (rm ) z

T 4 h

(T Tw ) H b (rm ) D z

(5.186)

(5.187)

Note that the global rate (–rm) is expressed per unit mass of catalyst. Isothermal fixed-bed operation First-order reactions without internal mass transfer limitations A number of reactions carried out at high temperatures are potentially mass-transfer limited. The surface reaction is so fast that the global rate is limited by the transfer of the reactants from the bulk to the exterior surface of the catalyst. Moreover, the reactants do not have the chance to travel within catalyst particles due to the use of nonporous catalysts or very fast reaction on the exterior surface of catalyst pellets. Consider a first-order reaction A ; B or a general reaction of the form aA ; bB ; products, which is of first order with respect to A. For the following analysis, a zero expansion factor and an effectiveness factor equal to 1 are considered. We should recall that in such a case, rov (rm ) kovCA b

(5.188)


409

where CAb is the bulk fluid-phase concentration and 1 1 1 kov km kf as

(5.189)

Consequently,

us

dC b kovCA b dz

(5.190)

Integrating with the condition, at z 0, CAb CAi,  k  CAb exp  b ov Z  CA i us  

(5.191)

 k   1  k

CAb exp  Z  ln  Z CA i  1 xA  us,i  us 

(5.192)

or

where Z is the bed height and

1 1 1 1 = = + o k ρbk ov ρbkm k f ρb as au

(5.193)

The coefficient ko introduces the external surface area of catalyst per unit volume of reactor u, which is more appropriate in fixed- and trickle-bed reactor analysis. External mass transfer–limited reactions In the expression (5.191), km has to be known, but it is not necessary if the external mass transfer phenomena are very intense. Actually, if strong mass resistance exists, the knowledge of the rate law is not essential, because the rate can be written as rov rf kf s (CA b CAs )

(5.194)

In most cases, the surface concentration is far less than the bulk concentration and we can consider CAb CAs ⬵ CAb rov kf sCAb

(5.195)

410

5. Catalysis

Then, eq. (5.190) can be rewritten as

us

dC b kf sCA b kf uCA b dz

(5.196)

and finally,  k  CAb exp  f u Z  CAi  us 

(5.197)

where CA,i is the inlet concentration of solute. First-order reactions with internal mass-transfer limitations Since the following equation is valid, under the assumption made earlier, we only have to replace the right form of the global rate as expressed in eq. (5.196):

us

dC b (rm ) dz

(5.198)

and (rm ) ov ks Ss,in CAb

(5.199)

where Ss,in is the internal specific surface area of the catalyst and ks the rate coefficient per unit surface of the catalyst. Recall eq. (5.87): ov 1

s s kvsVp

(5.200)

kf Sex

From eq. (3.10): kvsVp ks Sin

(5.201)

where Sin is the internal surface area of the catalyst. Then, eq. (5.200) becomes ov

s s kS k ( S V ) 1 s s in 1 s s in R kf Sex kf (Sex VR )

(5.202)


411

Here, note that (Sex) and (Sin) refer to properties of a single particle. Then Sin VR (nSin ) VR Sex VR (nSex ) VR

(5.203)

where n is the number of catalyst particles in the bed. This manipulation is convenient because we can now express the above ratio in terms of fixed-bed properties: (nSex)/VR u

(5.204)

Recall that b

nM p VR

VR

nM p

(5.205)

b

where Mp is the mass of a single particle. Then (nSin ) VR

nSin S in b Ss,in b nM p Mp b

(5.206)

So, eq. (5.203) becomes (nSin ) VR Ss,in b u (nSex ) VR

(5.207)

and finally ov can be obtained from ov

s kS 1 s s s,in b k f au

(5.208)

After integrating eq. (5.198), we obtain  b ks Ss,in ov CAb exp  CAi us 

 z 

(5.209)

and the conversion achieved at the reactor’s exit, z Z, is  b ks Ss,in ov  Z x 1 exp  us  

(5.210)

412

5. Catalysis

Note that if in the expression of ko we include the effectiveness factor s, 1 1 1 k k f u s k m b

(5.211)

then eq. (5.210) is equivalent to eq. (5.192) because b ks Ss,in ov k

(5.212)

ks Ss,in km

(5.213)

since

Then, for combined resistances (internal and external), we can use the following equivalent form:  k  CAb exp  Z  CA i  us 

(5.214)

where ko is obtained using eq. (5.211). Keep in mind that the analysis above is valid when the internal surface area of the catalysts constitutes the total surface area of the catalyst. This model is valid for a first-order reaction with zero expansion factor. In case of a variable-volume system, the following form of the material balance should be used (eq. (3.293)): dFA (R ) 0 dV

(5.215)

Q C dx dFA dx dx dx FAi FAi i Ai us,i CAi A dz dV dV d (zA) dz

(5.216)

Taking into account the fact that

and if A is the limiting reactant,

CAb CAi

1 xA 1 R xA

(5.217)


413

the solution of the model is  1  k Z (1 R )ln  R xA  us,i  1 xA 

(5.218)

Example 6 Managing all complexity: Calculation of the overall reaction rate combining external mass transfer, internal mass transfer, and reaction A waste gas stream containing 500 ppm of a VOC has to be treated in a fixed-bed reactor so that the concentration of VOC reduces to 50 ppm. A solid catalyst will be employed for the oxidation of VOC to CO2, at a temperature of 900 °C and a pressure of 1 atm. The reaction is first order with respect to the VOC concentration with a reaction rate coefficient ks 1.5 × 108 m3/m2 s. Given that volumetric feed rate 0.0072 m3/s particle density of catalyst 1000 kg/m3 NO diffusivity in air 2 × 107 m/s kinematic viscosity 1.53 × 108 m2/s solid specific area 200 m2/g pellet radius 0.005 m porosity 0.3 effective diffusivity of VOC 1.6 × 107 m2/s reactor radius 0.05 Calculate the weight of solids and the reactor length that are necessary for the desired conversion. Solution (1) Calculation of the internal effectiveness factor for spherical pellets and first order reaction: The Thiele modulus is

rp

km p

3

Deff

Recall that (eq. (3.10)) km ks Ss Then

s

rp

(ks Ss ) p

3

Deff

0.14

1 1 1 0.999  tanh(3) 3 

414

(2)

5. Catalysis

Calculation of the external mass transfer coefficient: A

D 2 0.00785m 2 4

us

Re

Q 0.917 ms A us dp

Sc

v

11,989

v 0.0765 Dg

Using eq. (3.352), we obtain  Re  Sh    

1 2

Sc1 3 84.9

Then,  Dg  kf   Sh 0.084 ms  dp 

(3)

Calculation of the external area per volume of solids

u

(4)

6 (1 ) 21,000 m 2 m 3 dp

Calculation of the overall effectiveness factor b p (1 ) 700 kgm 3

ov

s 0.998 kS 1 s s s,in b kf u


(5)

415

Calculation of the weight of solids for the specific conversion

x

W

(6)

CA,i CA,o CA,i

0.72

Q  1  ln   3080 g ov ks Ss,in  1 x 

Calculation of the reactor length Z

W 0.56 m A b

Reaction-limited second-order reaction For a second-order reaction of the form 2A ; products, or in the more general case of a reaction of the form aA bB ; products of second order with respect to A, the intrinsic reaction rate is 2 (rm ) s km CAs

(5.219)

The overall reaction rate based on the unit volume of reactor is 2 (RA ) b rm b s km CAs (kf au )(CAb CAs )

(5.220)

After the elimination of the surface concentration,

CAs

(kf au ) (kf au )2 4(kf au )( b s km )CAb

(5.221)

2 b s k m

If kf → ∞ or kf >> km, then by the rate-controlling step concept, the resistance to the overall rate is due to the intrinsic reaction rate and thus 2 (RA ) b s km CAb

(5.222)

For zero expansion factor, the model of the reactor is

us

dCAb 2 b s km CAb dz

(5.223)

416

5. Catalysis

On integrating, we obtain CAi CAb  CAi b s km    Z CAb us 

(5.224)

k C xA s m b Ai Z 1 xA us

(5.225)

or in terms of conversion,

where CA,i is the inlet concentration of the solute. Adiabatic fixed-bed operation An issue that has to be taken into account while analyzing adiabatic fixed-bed reactors is the importance of the axial dispersion of heat and concentration. Generally, axial dispersion can be neglected due to the high velocities and high depths of beds found in industrial applications. However, it is an assumption that has to be verified. The ratio of Z/dp can be used as a criterion to determine if axial dispersion has to be considered. If this ratio is higher than 150, the influence of concentration and temperature gradients on the axial direction can be considered negligible (see also Section 3.6.3). In contrast, if axial dispersion is to be taken into account, a system of differential equations comes up that can be solved only by numerical methods. Recall the plug-flow steady-state energy balance (eq. (5.187)): cp us

T 4 h

(T Tw ) H b (rm ) D z

(5.226)

In an adiabatic fixed bed, heat is not exchanged with the environment through the reactor wall. Note that for the derivation of eq. (5.226), it has been assumed that the flow is ideal plug flow and thus the radial dispersion term is eliminated; in an adiabatic fixed bed, the assumption of perfect radial mixing is not necessary since no radial gradients exist. Furthermore, reactors that have large diameters or are well insulated can be considered to approach adiabatic operation, and in this case the term 4h°(T –Tw)/D drops out. Consequently, the above equation further simplifies to (us )cp

dT H b (rm ) dz

where: T the fluid temperature, (K) cp the specific heat of the fluid (J/kg K) the fluid density (kg/m3)

(5.227)


417

H the heat of reaction (J/mol). Heat of reaction is negative for an exothermic reaction and positive for an endothermic reaction. b the bulk density of the bed (kg/m3) us the superficial velocity of fluid (m/s) (–rm) the rate of disappearance of reactant (m3/kg s). Note that in an adiabatic fixed bed, the temperature varies from inlet to the outlet of the bed and thus the fluid density, volumetric flow rate, and superficial velocity are not constant. However, the product us in the above equation is the mass flow rate per unit crosssectional area of the bed (kg/m2 s), which is constant throughout the bed length. The mass balance equation is (eq. (5.215)) F dx dF (R) i b (rm ) dV A dz

(5.228)

where Fi is the inlet molar feed concentration of the reactant (mol/s) and A the cross-sectional area of the bed (m2). This form of the material balance is the appropriate one as it is applicable for variable-density systems as is the case of the adiabatic fixed beds. Substituting (–rm) from eq. (5.228) in the energy balance equation (5.227), we get (Smith, 1981) dT

H  Fi  H  Fi  dx dx   cp  Fm  cp  Aus 

(5.229)

where Fm is the total mass flow rate of the reaction mixture Aus in kg/s, which is constant throughout the bed length. Integrating this equation under the assumption of constant H and cp, To Ti

H Fi x cp Fm

(5.230)

Taking into account that at steady state at any point in the reactor, Fm Aus Aius,i, we have

Fi QC QC C  Q  Ci = i i = i i =  i = i   ρi Fm Aρ us Aρi us ,i A ρi us ,i

(5.231)

us,i

Then, we can derive the following equivalent form: To

HCi x Ti i cp

(5.232)

418

5. Catalysis

where: Ci the concentration of the reactant at reactor feed (mol/m3) i the inlet fluid density (kg/m3) x the conversion (–) Ti the temperature at reactor inlet (K) To the temperature at reactor outlet (K). Now, the global rate can be estimated at any conversion, since temperature can be calculated from eq. (5.232). Then, the conversion versus reactor depth or catalyst mass can be determined from the mass conservation equation (5.228). Only arithmetic solutions of the adiabatic model are possible. For the special case of a first-order reaction, the mass balance equation (5.228) becomes  1 x P Ti  dx b km (T )  dz us,i  1 R x Pi T 

(5.233)

where us,i is the inlet superficial velocity of the fluid (m/s) and km is the rate coefficient which is a function of temperature:  E  km (T ) ko exp  a   RT 

(5.234)

where ko is the preexponential factor and Ea the activation energy in the Arrhenius equation. Eq. (5.233) represents the general case of a nonisobaric, nonisothermal process with expansion factor. Note that for a liquid-phase reaction without phase change, the term in parenthesis becomes equal to 1 – x, even if the temperature and/or the pressure in the reactor is not constant. The effect of pressure is nearly always negligible in systems involving liquid components while the effect of temperature on the reactor performance is expressed through the energy balance equation (5.232) and eq. (5.234). Furthermore, eq. (5.233) is valid for a reaction-limited system, i.e. the controlling mechanism is the reaction step. Furthermore, the effectiveness factor is unity. The situation becomes more complex in the case where internal and external resistances exist as the effectiveness factor and the mass transfer coefficient should be taken into account (see eq. (5.211)) and they are a function of temperature. Example 7 Zhang and Hu (2004) studied the catalytic oxidation of carbon monoxide: CO(g) 12 O2 (g)CO2 (g) The catalyst used is commercial Type 111 Cu/Cr/Ag impregnated coal-based carbon of 0.3 mm particle size and bulk density of 700 kg/m3. This study confirmed that for this particle


419

size, the internal diffusion is negligible and the reaction is first order in respect to CO concentration. At 50 °C, the reaction coefficient km 2 × 106 m3/kg s and the activation energy Ea 107.23 kJ/mol. Then, assume that the reaction takes place in a fixed bed of 1.61 m diameter and 16.1 m height, under contact time of 5 min, and the inlet temperature of gas being 50 °C, for different CO inlet concentration (several runs). Estimate the conversion of CO in an isothermal and adiabatic fixed-bed reactor and under the following assumptions: isobaric process, negligible external mass transfer resistance, and approximately constant heat capacity of air (cp 1 kJ/kg K) and heat of reaction (H –67,636 cal/mol). The inlet temperature of the reaction mixture is 50 °C and its composition is 79% N2 and approximately 21% O2, while the inlet CO concentration varies from 180–4000 ppm (mg/kgair) (for each individual run). Assume that the particles are spherical. The experimental conversion of CO at 50 °C and for 5 min is approximately 0.38 (isothermal operation and 180 ppm initial CO concentration). The density of air at 50 °C is 1.09 kg/m3. Solution At first it should be mentioned that due to the great excess of inerts (79%) and low concentration of CO (180–4000 ppm), the expansion factor could be taken equal to zero. Isothermal fixed bed: Since the phenomenon is rate-limited,

1 1 1 1 ⬵ k 1.4 103 s1 o k k a k k b m f u b m The volume of the bed is 32.76 m3 and its cross-sectional area 2.04 m2 and thus, VR V 32.76 Q R 0.11m 3 s (560) Q Q 0.11 us 0.054 ms A 2.04

Then, eq. (5.192) becomes  1  b km Z x 0.343 ln   1 x  us which is close to the experimental value of 0.38. This indicates that the assumptions of unity effectiveness factor, rate-controlling mechanism, and zero expansion factor are close to reality. Note that the conversion in the isothermal fixed beds is the same for different CO inlet concentration.

420

5. Catalysis

Adiabatic fixed bed: Since • • • •

the controlling mechanism is the reaction step the effectiveness factor is unity the expansion factor is zero (R 0) the operation is isobaric (Pi P),

the material balance equation (5.233) becomes dx b dz us,i

 Ti  Ea    ko exp  RT   冸1 x 冹 T  

where ko is the preexponential factor and Ea the activation energy in the Arrhenius equation. We have Ea 107,230 39.93 RT 8.314(273 50) and thus  E  km ko exp  a  ko 4.391011 m 3 kg s  RT  At a distance z corresponding at a conversion level x from the bed inlet and for constant H and cp (eq. (5.232)), T

HCi x Ti i cp

The final equations to be solved are  dx  12897.5   (1 x ) 1.851018 exp    dz T   T  T 323 259.08Ci x The solutions are only arithmetic and Polymath software has been used. Note that T is in K and that the concentration of CO is in mol/m3. The conversion of ppm into mol/m3 is done by using the molar weight of CO (28 kg/kmol) and the density of air (1.09 kg/m3).


421

In Figure 5.7, the final conversion and exit temperature is plotted against the inlet CO concentration. As we see, for a specific reaction, the higher the inlet concentration, the higher the conversion and the exit temperature. This is a result of the positive effect of the temperature rise, due to the exothermic nature of the reaction, on the rate coefficient and thus on the reaction rate and conversion. Note that for higher inlet CO concentration, the conversion for the isothermal operation is the same, while for the adiabatic operation the conversion is higher for higher inlet concentrations. Furthermore, the conversion in the adiabatic fixed bed is always higher in comparison to the isothermal fixed bed. Of course, these results are such because the reaction is of first order in respect to CO. In Figure 5.8, the conversion and temperature profile in the reactor versus the distance from the reactor length for inlet CO concentration of 4000 ppm is given. As expected, the conversion and the temperature are higher as the reaction mixture approaches the reactor exit. This is true irrespective of the inlet concentration of CO. Monolithic catalysts Monolithic catalysts have found a wide range of applications in the removal of pollutants from air, especially in the automotive industry. Specifically, the demand for large surface to small volume, high conversions achieved for low retention times, and low pressure drop led to the development of monolithic supports. More information on automotive catalytic converters has been given in Chapter 1. Usually, a thin layer of alumina is deposited onto a monolith for keeping the precious metal used for air pollutants abatement dispersed. The oxidations that take place are highly exothermic and the reaction rates achieved are in turn high. Hence, the reactants diffuse only a small distance

100

80

60 x (%) Tout (oC) 40

20 0

1000

2000

3000

4000

ppm CO

Figure 5.7

Final conversion and exit temperature versus inlet CO concentration.

422

5. Catalysis

100

80

60 x (%) T (oC)

40

20

0 0

6

12

18

z (m)

Figure 5.8

Conversion and temperature profile in the reactor.

within the catalyst. Therefore, the intraparticle concentration and temperature gradients can be safely considered negligible in monolithic catalysts. On the other hand, the external mass and heat transfer resistances are significant. Moreover, heat transfer by conduction and radiation may take place due to the continuous form of the monolith and the high temperatures found in such catalysts. So, the mathematical models describing monoliths show some variations from those in classical packed beds. We will develop a model for monoliths based on the following assumptions (Hegedus, 1975): • • • • •

heat transfer by radiation is negligible the catalyst is continuous in the flow direction the flow follows plug-flow pattern the oxygen is in excess the reaction order is first order with respect to the concentration of pollutant: Q

dC kf L (C Cs ) 0 dz

(5.235)

where: Q the volumetric flow rate kf the mass transfer coefficient from gas to catalyst surface L the mass or heat transfer area per unit length of catalyst dz the section of catalyst length. For the solid phase, the mass concentration of the pollutant requires kf L (C Cs ) km Cs

(5.236)


423

where: km reaction rate coefficient per unit mass of catalyst at Ts catalyst mass per unit length of reactor. Taking into account the axial conduction of heat in the solid phase, the energy conservation equation for the gas is Qcp

dT h L (T Ts ) 0 dz

(5.237)

and for the catalyst,

x L

d 2Ts dz 2

h L (T Ts ) ( H )km Cs 0

(5.238)

where x is the thickness of the solid support. Neglecting the heat loss from the end of the catalyst, the boundary conditions are C Cin , T Tin , dTs dz 0 at dTs dz 0 at

z0

zL

Considering that the temperature is constant, the manipulation of the above equation leads to the following equation: dC C dz  1 1  Q  km kf L 

(5.239)

    C z  1 x exp   Cin  1 1   Q   km kf L   

(5.240)

Integrating this equation, we get

Now, the conversion x can be estimated at any reactor length z. Example 8 The oxidation of gaseous ethanol (first-order reaction) was studied in a spinning basket reactor with Pt/Al2O3 on a ceramic monolith as catalyst. The inlet concentration of ethanol

424

5. Catalysis

Table 5.9 Experimental results Experiment number

Rotation speed (rpm)

CE (mol/cm3)

T (°C)

1 2 3 4 5 6 7

80 120 240 400 450 600 600

0.0180 0.0167 0.0149 0.0138 0.0135 0.0135 0.0087

203 203 203 203 203 203 228

Note: T is the reactor temperature and CE the concentration of ethanol at the reactor outlet.

was 0.0242 mol/cm3 in nitrogen, whereas oxygen was in great excess. The feed flow rate was 750 cm3/min at 25 °C and 1 atm. The catalyst was placed in two baskets and had a total mass of 2 g. The external area of the catalyst was 20 m2. The reactor operated isothermally. The results obtained are shown in Table 5.9. Find: (a) (b)

the activation energy of ethanol oxidation, the coefficient of mass transfer to the catalyst surface with respect to the rotation speed of the catalyst.

Solution (a) The specific type of reactor is appropriate for kinetic analysis because it can be operated under conditions of absence of external phenomena of mass and heat transfer. By adjusting the rotation speed of the catalyst, the stagnant film around the catalyst can be practically eliminated, and thus the resistance of mass and heat transfer from the bulk of the fluid to the external area of the catalyst can be neglected. Moreover, the resistance to the diffusion of the fluid inside the pores of the catalyst and the intraparticle gradients of concentration and temperature in general can also be considered negligible since the depth of the washcoat Al2O3, where the active metals are dispersed, is very small, about 50 m (Smith, 1997). The energy balance of ethanol in this type of reactor (CSTR type) is flux in – flux out disappearance by reaction QCEo QCE (rm )W and thus (rm )

Q (CE i CE ) W


425

4.50

rm (µmol/(min. gcat))

4.00

3.50

3.00

2.50

2.00 0

100

Figure 5.9

200

300 400 rotation speed (rpm)

500

600

700

The reaction rate versus the rotation speed at 203 °C.

where: (–rm) the reaction rate per unit mass of catalyst, mol/(min gcat) CEi the concentration of ethanol at the reactor inlet, mol/cm3 CE the concentration of ethanol at the reactor outlet, mol/cm3 Q the feed flow rate, cm3/min W the mass of catalyst, g It is obvious that the calculation of the reaction rate is very easy in this reactor. Now, let us sketch the reaction rate versus the rotation speed using the data obtained at 203°C. As shown in Figure 5.9, the reaction rate is stabilized at rotation speeds above 400–450 rpm. This means that the external mass transfer does not affect the global rate, and thus values of the intrinsic rate can be safely considered to be obtained at those rotation speeds. Consequently, the activation energy will be determined by means of the data obtained at 600 rpm, where the values of the observed rate represent the actual intrinsic rate. Since the reaction is of first order with respect to ethanol, (rm ) km CE and km ko eEa RT Applying the first equation for 203 and 228 °C and dividing, we obtain km,203 CE,203 km,228 CE,228

Q (CEi CE,203 ) W (rm,228 ) Q (CEi CE,228 ) W (rm,203 )

426

5. Catalysis

or km,203 0.0135 4.0125 km,228 0.0087 5.8125 km,203 km,228

0.4449

ko eEa 1.987(273203) ko eEa 1.987(273228) eEa 1.987(273203) eEa 1.987(273228)

0.4449

0.4449

Ea  1 1    ln(0.4449) 1.987 273 228 273 203  Finally, we obtain the activation energy Ea 15,474 cal/mol. Substituting the value of the activation energy in km,203CE,203 (rm,203 ) ko e15474 1.987(273203)CE,203 (rm,203 ) and finally, the preexponential is also obtained: ko 3.79 × 109 cm3/(gcat min). (b) Now, we can use the intrinsic reaction rate coefficient to estimate the mass transfer coefficient at the various rotation speeds of the catalyst. Recall that (rm )

1 1 1 km kf as

Cbulk

for a first-order catalytic reaction when the reaction is considered to take place at the external surface of the catalyst, which is the case for a monolith. Since the mass of the catalyst is 2 g and its external area 20 m2, it can be seen that as 10 m2/gcat 105 cm2/gcat. Moreover, Cbulk CE. After some manipulations, the following can be easily derived for the region of external mass transfer existence: kf

So, Table 5.10 can be formed.

km (rm ) s 冤 km CE (rm )冥


427

Table 5.10 The value of kf for each catalyst rotation speed Experiment number

Rotation speed (rpm)

kf (cm/min)

1 2 3 4

80 120 240 400

0.002283 0.003883 0.01099 0.05681

0.06

y = 0.0011e0.0098x R2 = 0.9978

0.05

kf (cm/min)

0.04 0.03 0.02 0.01 0 0

50

100

150

Figure 5.10

200 250 300 rotation speed (rpm)

350

400

450

kf versus catalyst rotation speed.

Now, since km has been estimated for each catalyst rotation speed, the relation between km and rpm can be easily deduced using an exponential equation, as shown in Figure 5.10. Consequently, kf 0.0011e 0.0098 N where N is the rotation speed in rpm. Non-isobaric fixed-bed operation The following analysis focuses on catalytic processes involving a gas phase. However, the principles are applicable in the case of the adsorption of solutes from gas phase. Reactor model with pressure drop

Consider the gas phase reaction aA bBcC

428

5. Catalysis

where A is the limiting reactant. In the general case of nonisothermal, nonisobaric, and variable-density reaction (eq. (3.91)), P T V (1 R xA ) i Vi P Ti

(5.241)

1 xA P Ti P T f ( xA , , ) 1 R xA Pi T Pi Ti

(5.242)

and thus, the concentration is CA CA,i

To simplify the analysis, the operation is considered isothermal (T Ti). Reactor model for a first-order reaction: To illustrate the effect of pressure drop, consider an isothermal two-phase fixed-bed operation (gas–solid system). In terms of a reactant, the intrinsic reaction rate is (rm ) km CAS

(5.243)

The rate per unit volume of reactor is (RA ) b (rm ) b km CAS (kf au ) 冤CA CAS 冥

(5.244)

Following the well-known procedure for the elimination of surface concentration, we have CAS

( k f au ) CA (kf au ) b km

(5.245)

Then, the rate in terms of the fluid bulk-phase concentration (overall rate per unit volume of reactor) is P  (RA ) ( b km )CAS k CA k f  xA ,  Pi  

(5.246)

1 1 1 k ( k f au ) b k m

(5.247)

where

Here, CA denotes the bulk -phase concentration and ko the overall kinetic coefficient. According to Fogler (1999), the differential form of the design equations should be used


429

whenever pressure-drop effects are accounted for. Then, the design equation per unit volume of a fixed-bed reactor is (eq. (3.293)) dFA dx dx (RA ) (RA ) 0 FA,i A (RA ) 0 FA,i A 0 dV dV dW b (5.248) Or P dxA k

 f xA ,  Pi  dW b FA,i 

(5.249)

In terms of the reactor length, P dxA dx Ak

k  f  xA ,  A Pi  FA,i dV FA,i  dz

P  f  xA ,  Pi  

(5.250)

Model for a second-order reaction with negligible resistance in the gas film (kf ; -): In this case, the intrinsic reaction rate is 2 (rm ) km CAS

(5.251)

The overall reaction rate based on unit volume of reactor is 2 (RA ) b rm b km CAS (kf au )(CA CAS )

(5.252)

and after the elimination of the surface concentration, we have

CAS

(kf au ) (kf au )2 4(kf au )( b km )CA 2 b km

(5.253)

It is obvious that this form of reaction rate becomes very complicated to work with when the surface concentration is introduced. In this case, we can use the concept of the ratecontrolling step. In our case, we assume that kf ; - or kf km, the resistance to the overall rate is owing to the intrinsic reaction rate. Then, due to the equality of the individual rates, if kf ; - then (CA – CAS) ; 0 or CA ; CAS, and so the individual rates are finite and equal to the overall rate. The rate in terms of the fluid bulk-phase concentration (overall rate per unit volume of reactor) is (RA ) b km CA2

  P b km  f  xA ,   Pi    

2

(5.254)

430

5. Catalysis

Note that here, km is found with the bulk concentration of fluid and not with the surface concentration as in the case of the first-order reaction. This is why in this example, the fluid-phase resistance has been eliminated. The design equation per unit volume of reactor is (eq. (3.293)) dFA dx dx (RA ) (RA ) 0 FA,i A (RA ) 0 FA,i A 0 dV dV dW b

(5.255)

or dxA k m dW FA,i

  P  f  xA , P   i  

2

(5.256)

In terms of the reactor length, dx A b k m dV FA,i

2

  P dxA A b km  f  xA , P   FA,i dz i  

  P  f  xA , P   i  

2

(5.257)

where A is the cross-section of the reactor. Pressure drop analysis For a fixed bed, the Ergun equation for pressure drop in a differential form is (eq. (3.302)) 150u (1 )2 1.75us2 (1 )  dP  2 2 s  s dp dz 3 3   s dp

(5.258)

where dp is the particle diameter for spherical particles or the nominal diameter for irregularshaped particles, the density of the fluid, g the gravity acceleration constant (9.81 m2/cm), the dynamic viscosity of the fluid, the fixed-bed voidage, us the superficial fluid velocity, and S the sphericity of the particle. In eq. (5.258), the pressure-dependent parameters are the superficial velocity and the gas density. In analogy to the reaction mixture volume,  Q   u P Q A s (1 R xA ) i Qi  Qi  us,i P   A

(5.259)

while for gas density,

MG  MG  i 1 P P   V  Vi  (1 R xA ) Pi (1 R xA ) Pi

(5.260)


431

or 1 P i (1 R xA ) Pi

(5.261)

where the subscript “i” denotes the value of the parameter at the inlet conditions. It is interesting to note here that from the last two equations, u P Q s i (1 R xA ) i Qi us,i P

(5.262)

which is the equation of continuity: Qi i Q us,i i us

(5.263)

or in words, under steady state conditions, the mass flow rate (kg/s) at any point of the reactor is constant and equal to the entering mass flow rate. Substituting in eq. (5.258) we have: 150us,i (1 )2  P dP  2 2  (1 R xA ) i 3 dz P  s dp 

2 1.75 i us,i 2 (1 )  1 P 2  Pi  1 x  ( )  R A   3  P  (1 R xA ) Pi   s dp

(5.264)

or P dP a(1 R xA ) i dz P

(5.265)

冢冣

dP where a , is given by eq. (5.258) with i. Here note that dZ i • •

if R is negative, the pressure drop will be less than that for zero expansion if R is positive, the pressure drop will be greater than that for zero expansion.

The reactor length is b Z b

VB W W Z A A b A

(5.266)

432

5. Catalysis

where A is the cross-sectional area of the bed and VB its volume. Then, eq. (5.265) becomes dP 1  P g x , i  a dW b A  A P 

(5.267)

P P  g  xA , i  (1 R xA ) i  P P

(5.268)

P dP ag( xA , i ) dz P

(5.269)

and

In terms of the reactor length,

Now, from eqs. (5.249) and (5.267), or (5.250) and (5.269), it is clear that the reactor model becomes a system of two coupled differential equations, which should be solved simultaneously. Overall model for a first-order reaction: For the first-order reaction, we have

CA CA,i

1 xA P 1 R xA Pi

(5.270)

The rate becomes (RA ) k CA k CA,i

1 xA P 1 R xA Pi

(5.271)

Then, eq. (5.250) is  1 xA P dxA  Ak

k o 1 xA P  CA,i  dz  FA,i  1 R xA Pi us,i 1 R xA Pi

(5.272)

 1 xA P dxA  k o 1 xA P ko  CA,i  dW  b FA,i  1 R xA Pi bQA,i 1 R xA Pi

(5.273)

and eq. (5.249) is


433

The pressure-drop eqs. (5.267) and (5.269) are (1 R xA ) Pi dP a dW b A P

(5.274)

P dP a(1 R xA ) i dz P

(5.275)

Overall model for a second-order reaction: With negligible resistance in the gas film (kf → ∞): In this case, the rate becomes  1 xA P  (RA ) b km CA,i  1 R xA Pi  

2

 1 xA P  CA,i  1 R xA Pi  

2

(5.276)

Then, eq. (5.257) is dxA A b km dz FA,i

(5.277)

and eq. (5.256) is dxA k m dW FA,i

 1 xA P  CA,i  1 R xA Pi  

2

(5.278)

The pressure-drop eqs. (5.267) and (5.269) are (1 R xA ) Pi dP a dW b A P

(5.279)

P dP a(1 R xA ) i dz P

(5.280)

Pressure drop for zero expansion: In the case where • • •

the expansion factor R is zero or xA is low, so that R⋅xA 1 or there is no reaction.

From eq. (5.279), we have dP 1 Pi a dW b A P

(5.281)

434

5. Catalysis

or, in terms of bed length (eq. (5.280)), P dP a i dz P

(5.282)

In this case, the pressure drop is independent of the conversion and the two differential equations are decoupled. Fortunately, this is the case in many environmental applications where the gas species to be removed are in such low concentrations (large excess of inerts) that the expansion factor is practically zero. As pointed out in the introduction of this section, the basic principles of the analysis are also applicable in the case of adsorption of solutes from the gaseous phase. Again, for environmental applications, the concentration of solutes is so low that the pressure drop is only due to the flow of the gas. Here, the expansion factor has the same meaning, i.e. it measures the change of the volume of the gas phase, which is negligible in the case of low concentrations of the removed gas species. The solution of eqns. (5.281) and (5.282) is 2P 2W  P  P Pi a i W 1 a  b A Pi b A  Pi  2

2

P 2 Pi2 2 a Pi Z

P  2Z  1 a  Pi  Pi 

0.5

(5.283)

0.5

(5.284)

Note that when RxA 1, P i Pi

(5.285)

P (1Z )0.5 Pi

(5.286)

or

or in terms of the mass of the solid phase, P (1W )0.5 Pi

(5.287)

where 2a Pi

(5.288)

2a Pi b A

(5.289)


435

Then, eqs. (5.286) and (5.287) can be inserted into eqs. (5.250) and (5.249) , respectively to derive the design equation for the fixed bed. Approximate solution for a first-order reaction with zero expansion (R0): For a firstorder reaction, the solution of eq. (5.249) is  1  k

1 (1 W )1.5  ln    1 xA  1.5us,i 

(5.290)

or in terms of the reactor length,  1  k

1 (1 Z )1.5  ln     1 xA  1.5us,i 

(5.291)

Approximate solution for the second-order reaction with negligible resistance in the gas film and zero expansion (kf → ∞ and R0): In this case, the solution of eq. (5.249) is (Fogler, 1999) km CA,iW  xA W   1  1 xA Qi 2 

(5.292)

or in terms of the reactor length, km bCA,i Z  xA Z   1  1 xA us,i 2

(5.293)

Analysis of the models First-order reaction: Let us have a deeper look into the solution of the model for a first-order reaction with zero expansion (eq. (5-291)):  1  k

1 (1 Z )1.5  ln    1 xA  1.5us,i 

(5.294)

We can compare it with the well-known solution for fixed beds for no expansion of the gas phase (eq. (5.192):  1  k Z ln   1 xA  us,i

(5.295)

436

5. Catalysis

This solution ignores the pressure drop in the reactor. The difference of these two equations is that the first one has the following parameter instead of Z:

f (Z )

1 (1Z )1.5 1.5

(5.296)

The difference between these two equations can expressed as follows:

DZ

Z f (Z ) Z

(5.297)

Note that Z has certain limits originating from eq. (5.286) and as well as from f(Z): (1 Z ) 0 Z 1

(5.298)

Since Z ≠ 0 and ≥ 0, the above condition means that 0

1 Z

(5.299)

Now, we are interested in the limits of the function f(Z). It is easy to show that lim 0 f ( Z ) Z lim 1z f ( Z )

Z 1.5

(5.300)

(5.301)

Second-order reaction: For a second-order reaction with kf ; ∞ and zero expansion (eq. (5.293)), km bCA,i Z  xA Z   1  1 xA us,i 2

(5.302)

We can compare it with the solution for fixed beds for zero pressure drop and no expansion of the gas phase (eq. (5.225)): km bCA,i Z xA 1 xA us,i

(5.303)


437

This solution ignores the pressure drop in the reactor. The difference between these two equations is that the first one has the following parameter instead of Z: Z   g( Z ) Z  1   2

(5.304)

The difference between these two equations can expressed as follows: DDZ

Z g( Z ) Z

(5.305)

The limits originating from eq. (5.286) for Z still hold and thus the limits of the function g(Z) are lim 0 f ( Z ) Z

(5.306)

Z 2

(5.307)

lim 1z f ( Z )

which are similar to the first-order reaction rate. To illustrate the situation we set Z 1. Then the condition becomes 0 1 And the limits of the function f(Z) for the first-order reaction are lim 0 f ( Z ) 1 lim 1 f ( Z ) 0.67 The corresponding limits for DZ are 0 and 0.33, respectively. For the second-order reaction, lim 0 g( Z ) 1 lim 1 g( Z ) 0.5 The corresponding limits for DDZ are 0 and 0.5 respectively. This is shown in Figure 5.11. The pressure drop in the case of reaction: The above analysis means that when ; 0 the two model solutions converge, for both cases, i.e. the solution becomes the same as the one considering negligible pressure drop. Actually what happens is  2a   a  P 0 P 0 i

i

(5.308)

438

5. Catalysis

1 0.9 0.8 0.7 0.67 0.6

f(Z)

DZ

g(Z)

DD Z

0.5

0.5 0.4

0.33 0.3 0.2 0.1 0 0

0.2

Figure 5.11

0.4

γ

0.6

0.8

1

The limits of f(Z), g(Z), DZ, and DDZ versus .

which means that the inlet pressure is high and/or the pressure gradient in the reactor based on inlet density is relatively low, and from eq. (5.286) we have

Pia Z0 1 Z1

P 1 Pi

(5.309)

or in other words, the pressure drop in the reactor becomes negligible. On the other extreme, when ; 1/Z, the deviation between the two model solutions is significant. What happens is that  2a  1  a  0.5  P  Z  P  Z i i

(5.310)

P (1Z )0 0 Pi

(5.311)

From eq. (5.286) we have

or in other words, the pressure drop in the reactor becomes very high. This is why Smith (1981) states: “In most cases pressure drop is small with respect to the total pressure so


439

that ignoring this effect is justified. However, for gaseous reactions at low pressures the change in pressure may affect the global rate significantly.” As engineers, we need some approximate values not only for design purposes but also to have a sense of the parameter’s magnitude. Thus, to go one step further in our analysis, we would like to answer the question “what is the practical limit above which the two solutions deviate considerably,” or in other words “what should be limit of the pressure drop in the reactor in order to assume that its effect is negligible.” First-order reaction: Now, first of all we have to define what is a “considerable difference.” For a first order reaction with zero expansion this would be f ( Z ) 0.95Z or DZ

Z f (Z ) 0.05 Z

(5.312)

(5.313)

which means that f(Z) deviates from Z by less than 5%. The condition we set is the following: 1 (1 Z )1.5

0.95Z (5.314) 1.5 or, because is positive,

1 (1 Z )1.5 1.425Z

(5.315)

1 (1 Z )1.5 1.425Z 0

(5.316)

and rearranging,

It is easy to show that this function has two roots, i.e. Z 0 and 0.193 and for Z 0.193 the function becomes negative. From the definition of , Z

2a Z 0.193 Pi

(5.317)

or aZ 0.097 Pi

(5.318)

440

5. Catalysis

The pressure gradient a can be easily evaluated from the Ergun equation by setting i. By using eq. (5.286) and for (Z) 0.193: P 0.898 Pi

(5.319)

and the total pressure drop in the reactor is P Pi P 0.102 Pi

(5.320)

P 0.102 Pi

(5.321)

or

which means that the total pressure drop should be less than 10.2%, and in this case the pressure-drop effect on the reactor model can be neglected. Second-order reaction: As in the previous case, the safety criterion could be g( Z ) 0.95Z

(5.322)

or

DDZ

Z g( Z ) 0.05 Z

(5.323)

which means that g(Z) deviates from Z by less than 5%. The condition we set is the following: Z   Z  1  0.95Z  2 (5.324) or, because Z is positive and nonzero, 1

Z

0.95 2

(5.325)

and rearranging, Z 0.1

(5.326)


441

From the definition of , Z

2a Z 0.1 Pi

(5.327)

or aZ 0.05Pi

(5.328)

The pressure gradient a can be easily evaluated from the Ergun equation by setting i. By using eq. (5.286) and for (Z) 0.1, P 0.949 Pi

(5.329)

and the total pressure drop in the reactor is P Pi P 0.051Pi

(5.330)

P 0.051 Pi

(5.331)

or

which means that the pressure drop should be less than 5.1%, and in this case the pressuredrop effect on the reactor model can be neglected. The pressure drop in the absence of reaction: In a fixed bed where no reaction takes place, the outlet pressure can be evaluated as follows:  P  2 aZ   P   1 P  i A i

0.5

(5.332)

where a Z 0.5 Pi

(5.333)

Assuming that the density is constant, we can use the Ergun equation to evaluate the exit pressure as follows. We have P a Z

(5.334)

442

5. Catalysis

then  P aZ P Pi aZ   1 Pi  Pi  E

(5.335)

we define the difference as  P  P  2 aZ       1 Pi   Pi  A  Pi  E 

0.5

 aZ   1  Pi  

(5.336)

From this equation, it can be proved that is negative and thus the outlet pressure derived from the Ergun equation is always higher than the actual one, or in other words, the pressure drop is lower than the actual one. This is shown in Figure 5.12, where

%

100 冢 P Pi 冣 A

(5.337)

1000 100 10 1 %ω

0

0.1

0.2

0.3

0.1 0.01 0.001 0.0001 0.00001 aZ/Pi

Figure 5.12

% versus aZ/Pi

0.4

0.5


443

1000.00 u max (cm/s) τ(s) 100.00

10.00

1.00

0.10 0.00

0.50

1.00

1.50

2.00

2.50

dp (mm)

Figure 5.13 The effect of particle density on the limiting contact time in a typical fixed bed (air at 20 °C, Pi = 1 atm, ε = 0.5, p = 2 g/cm3, Z = 1 m).

As can be seen, aZ/Pi 0.1 seems to be an adequate upper limit to assume the error in the pressure drop estimation introduced by assuming constant fluid density to be negligible (% is lower than about 0.64%). As has been analyzed previously, the limiting value of aZ/Pi is different (lower) in the case of a reacting system. This is why the analysis here is on the differences in pressure-drop estimation originating from the assumption of constant fluid density in a non-reacting system, while in reacting systems the analysis is on the effect of zero pressure drop assumption in the simplified models. In Figure 5.13, the maximum superficial velocity and the corresponding minimum contact time for keeping aZ/Pi lower than 0.1 is shown for a typical fixed bed with no reaction. It is clear that the limit in aZ/Pi can be easily satisfied in fixed beds with particles larger than about 1 mm, where the bed can be operated in a wide range of superficial fluid velocities up to values in the vicinity of 100 cm/s. Example 9 Suppose we have measured the total pressure drop in a packed bed of known length. Then, one expressed the opinion that to calculate the pressure gradient, we have to only divide the pressure drop with the bed length. Do you agree? Solution Pressure drop is a function of bed length and in the general case, P f (z)

444

5. Catalysis

Then, by diving the total pressure drop by the bed length we obtain P f ( z ) Z Z Furthermore, P P Pi P Pi f ( z ) Then, the pressure gradient is dP d冤 Pi f ( z )冥 f ( z ) dz dz Consequently, dP P " dz Z unless f ( z )

f (z) Z

which is true only for a linear function of the form f ( z ) az This is the case where the total pressure drop is a linear function of bed length, or P aZ Then, by diving the total pressure drop by the bed length we obtain: P a Z Furthermore, P P Pi P Pi aZ


445

Then, the pressure gradient is dP d冤 Pi az 冥 a dz dz Consequently, dP P dz Z In this special case, the pressure gradient is independent of the bed length and this could be the case only if the expansion due to reaction is zero, or in absence of any reaction and the constant fluid density throughout the reactor volume. Then, it is clear that the general impression that the pressure gradient is equal to the total pressure drop divided by the bed length it is true only under certain conditions. In the case of a reaction with no expansion or in absence of any reaction, by using eq. (5.286), P P Pi Pi 1 (1 Z )0.5  Then, by diving the total pressure drop by the bed length, we get 0.5 P Pi 1 (1 Z )  Z Z

Furthermore, by using eq. (5.286), P P Pi P Pi Pi 1 (1 Z )0.5  P Pi (1 Z )0.5 Then, the pressure gradient is 0.5 Pi dP d  Pi (1 Z )  dz dz 2(1 Z )0.5

And thus dP P " dz Z

446

5. Catalysis

Example 10 Consider the following typical reaction in petroleum reforming process: paraffinolefin H 2 which can be considered a first-order reaction in respect to paraffin. The reaction constant is 2 × 105 m3/kg s and the internal effectiveness factor 0.5. The reaction takes place in a fixed bed 20 m long and 3 m in diameter. The spherical particles used are of 1.5 mm diameter and 2.6 g/cm3 density. The bed voidage is 0.4. Pure paraffin enters the reactor with a rate of 0.25–1.55 m3/s and pressure of 10 atm. The density of paraffin is 0.032 g/cm3, its viscosity 1.5 × 105 kg/m s, the diffusion coefficient 0.1 cm2/s, and the average molecular weight 0.1 kg/mol. Calculate the conversion of paraffin in the following cases: • • • •

Using the real R and pressure drop (case 1) Assuming R 0 and taking into account the pressure drop (case 2) Assuming R 0 and ignoring pressure drop (case 3) Using the real R and ignoring pressure drop (case 4)

Solution Reactor models:At first, we write the general model for the reactor consisting of two coupled differential equations. The mass balance for a first-order reaction, eq. (5.272): dxA k 1 xA P dz us,i 1ε R xA Pi The pressure drop equation (5.275): P dP a(1ε R xA ) i dz P where for spherical particles S 1 and thus (eq. (5.258)) dP 150us,i (1 ε)2 1.75 i us,i (1 ε) dz dp ε3 ε3 dp2 2

a

Furthermore (eq. (5.211)), 1 1 1 k (k a ) k f u b m


447

Then, we have the following simplified models. For zero expansion and taking into account the pressure-drop effect, the solution is (eq. (5.291))  1  ko 冤1 (1 Z )1.5 冥 ln   1 xA  1.5us,i where (eq. (5.288))

2a Pi

For zero expansion and ignoring the pressure-drop effect, the solution is (eq. (5.192))  1  k

Z ln   1 xA  us,i Taking into account the expansion and ignoring the pressure-drop effect, the solution is (eq. (5.218))  1  k

Z (1 ε R )ln  ε R xA  us,i  1 xA  It is evident that for case 1 we need an arithmetic solution, and a software package like Polymath can be used. The rest of the cases have analytic solutions. Parameters of the system: For the specified reaction 1 (eq. (3.87)) and since we have a pure gas in the feed, R 1 (eq. (3.88)). The rest of the parameters we need are shown in Table 5.11 for three different volumetric flow rates. For the mass transfer coefficient, the correlation of Petrovic and Thodos is used (eq. (3.353)). From the values of the Table 5.11, it is clear that the mass transfer coefficient is quite high and its contribution to the overall coefficient ko is negligible, and thus the reaction on Table 5.11 The values of the parameters Qi (m3/s)

kf (m/s)

ko (s–1)

a (Pa/m)

0.25 0.5 1 1.5 1.55

0.0443 0.069 0.107 0.139 0.156

1.56 × 102 1.56 × 102 1.56 × 102 1.56 × 102 1.56 × 102

637 2151 7808 16971 18080

448

5. Catalysis

the surface is the rate-controlling mechanism. By using these parameters and the data given in this example, the conversion of paraffin is shown in Table 5.12. The total pressure drop is evaluated by the general model (case 1), i.e. is the real one. The general model, case 1 (eqs. (5.572) and (5.575)) can be solved only by numerical methods, e.g. Polymath software. For better inspection, the results are shown graphically in Figure 5.14. From the results, the following is evident •

The models of cases 1 and 4 give approximately the same results up to a pressure drop of 27% (1 m3/s). The difference between these two models is that in case 4, the pressure-drop effect is ignored. Thus, even for relatively high pressure drop, a simplified model that takes into account the expansion but not the pressure drop is able to approximate the real conversion.

Table 5.12 The conversion for each case Qi (m3/s)

P (%)

Case 1

Case 2

Case 3

Case 4

0.25 0.5 1 1.5 1.55

2 7 27 73.2 87.2

0.99 0.93 0.74 0.55 0.53

1 0.99 0.87 0.69 0.67

1 0.99 0.89 0.77 0.76

0.99 0.93 0.77 0.65 0.64

1 case 1 case 2 0.9

case 3 case 4

XA

0.8

0.7

0.6

0.5 0

0.5

1 Qi

Figure 5.14

1.5

2

(m3/s)

The conversion versus volumetric feed for each case.


•

449

The models of cases 2 and 3 give approximately the same results up to a pressure drop of 27% (1 m3/s). The difference between these two models is that in case 4, the pressure-drop effect is ignored. Again, the pressure-drop effect seems to be of less importance for this level of pressure drop. Of course, the differences between these two models and not to their performance is being referred to, as we see that they deviate a lot from the general solution in the whole region of volumetric flow rates.

It is clear that ignoring the expansion, the deviation is very serious for the whole region of volumetric flow rates while ignoring the pressured-drop effect, but using the expansion factor the results could be very good up to a certain pressure-drop level, which is approximately 27% in our example. 5.3.5 Trickle-bed reactors In the following sections, the solutions of the models as well as examples will be presented for the case of trickle-bed reactors and packed bubble bed reactors. Plug flow and firstorder reaction will be assumed in order to present analytical solutions. Furthermore, the expansion factor is considered to be zero unless otherwise stated. Some solutions for other kinetics will be also given. The reactant A is gas and the B is liquid unless otherwise stated. Conservation equations for each flowing phase—Smith’s approach Pure component in gas phase and saturated liquid phase First-order kinetics in A: In this case, the gas phase is a pure component A (CAG is constant) and the liquid phase is considered to be saturated with A (CAL is constant). Furthermore, the intrinsic rate is considered to be of first order with respect to A: –rm kmCAS, per unit mass of catalyst. Under these conditions, the material balances for the gas component A in the gas and liquid phases (eqs. (3.365) and (3.367)) are not needed: CAL is constant and equal to CALeq CAG/HA. The same analysis is valid for reactions of first order for both components, if CBL CAL and thus CBS ≅ const. (pseudo-first order) (Smith, 1981). Then (eq. (3.369)) (kf au )A 冤CAL CAS 冥 b s km CAS

(5.338)

or

CAS

 CAG  ( k f au ) A ( k f au ) A CAL ( k f a u ) A b s k m (kf au )A b s km  H A 

(5.339)

And thus

(kf au )B 冤CBL CBS 冥

b s km CAS 1 (kf au )A b s km  CAG  a a (kf au )A b s km  H A 

(5.340)

450

5. Catalysis

where s is the internal effectiveness factor. The overall coefficient ko is k

( k f a u ) A b s k m 1 1 1 ( k f a u ) A b s k m k ( k f a u ) A b s k m

(5.341)

Then using the mass conservation in the liquid phase for the liquid reactant B (eq. (3.368)),

usL

dCBL dCBL k o  CAG  (kf au )B 冤CBL CBS 冥 0 usL  0 dz dz a  H A 

(5.342)

The boundary (feed) condition is CBL CBL,f at z 0. Then, the integration of the previous equation yields

CBL CBL,f

k Z  CAG  ausL  H A 

(5.343)

where Z is the catalyst bed depth. In terms of the fractional removal of B:

xB

CBL,f CBL CBL,f

k Z  CAG  ausL  CBL,f H A 

(5.344)

First order kinetics in B: As in the previous case, the gas phase is composed of a pure component A (CAG is constant) and the liquid phase is considered to be saturated with A (CAL is constant). However, in this case, the intrinsic rate is considered to be of first order with respect to B: –rm kmCBS, per unit mass of catalyst. Under these conditions, the same analysis is valid for reactions of first order for both components, if CBL is very low, i.e. the reaction rate is pseudo-first order in respect to B (Smith, 1981). Furthermore, the analysis is valid for reactions of the type (Wu, 1996) A(liquid)C (liquid) D(gas) Then (kf au )B 冤CBL CBS 冥 b s km CBS

(5.345)

or CBS

( k f au ) B CBL (kf au )B b s km

(5.346)


451

The overall coefficient ko is k

( k f a u ) B b s k m 1 1 1 ( k f a u ) B b s k m k ( k f a u ) B b s k m

(5.347)

Then using the mass conservation in the liquid phase for the liquid reactant B (eq. (3.368)),

usL

dCBL dCBL (kf au )B 冤CBL CBS 冥 0 usL k CBL 0 dz dz

(5.348)

The boundary (feed) condition is CBL CBL,f at z 0. Then, the integration yields  k Z  CBL exp  CBL,f  usL 

(5.349)

where Z is the catalyst bed depth. In terms of the fractional removal of B,

xB

CBL,f CBL CBL,f

 k Z  1 exp   usL 

(5.350)

or  1  k Z ln   1 xB  usL

(5.351)

Pure component in gas phase and first-order kinetics in the gas reactant In this case, the gas phase is a pure component A (CAG is constant) and the concentration of A in the feed liquid phase is considered to be zero (unsaturated liquid phase). Furthermore, A is slightly soluble in the liquid (HA is large) so that KL ≅ kfg, while the intrinsic rate is of first order with respect to A: –rm kmCAS, per unit mass of catalyst. For reactant A in the liquid phase, at steady state (eq. (3.367)), usL

C  dCAL ( K L aGL )A  AG CAL  (kf au )A 冤CAL CAS 冥 0 dz  HA 

(5.352)

Then (kf au )A 冤CAL CAS 冥 b s km CAS

(5.353)

452

5. Catalysis

or CAS

( k f au ) A CAL (kf au )A b s km

(5.354)

or CAL CAS

b s k m CAL ( k f a u ) A b s k m

(5.355)

Then, the equation for reactant A in the liquid phase becomes

usL

C  dCAL ( K L aGL )A  AG CAL  k CAL 0 dz  HA 

(5.356)

k

( k f a u ) A b s k m 1 1 1 ( k f a u ) A b s k m k ( k f a u ) A b s k m

(5.357)

where

This equation could be integrated to give the following equation for CAL as a function of z:

CAL

 CAG  HA  

 z    1 exp 冤( K L aGL )A k 冥  usL      1  k (  K L aGL )A 

(5.358)

Then the mass conservation in the liquid phase for the liquid reactant B (eq. (3.368)) should be integrated:

usL

dCBL (kf au )B 冤CBL CBS 冥 0 dz

(5.359)

To do this, the surface concentration in the term in brackets must be eliminated. So, we use the equation (kf au )A 冤CAL CAS 冥 b s km CAS

(5.360)


453

or CAS

( k f au ) A ( k f au ) A CAL CAL ( k f a u ) A b s k m ( k f a u ) A b s k m

(5.361)

Then (kf au )B 冤CBL CBS 冥

b s km CAS 1 (kf au )A b s km ko CAL CAL a a a ( k f a u ) A b s k m

(5.362)

Then, using expression (5.358) derived above for CAL, (kf au )B 冤CBL CBS 冥 m冤1 exp(nz )冥

(5.363)

where the parameters m and n are  CAG  HA   k

m a 1  k

  ( K L aGL )A  n

( K L aGL )A k

usL

(5.364)

(5.365)

Then, the mass conservation equation for reactant B in the liquid phase becomes

usL

dCBL m冤1 exp(nz )冥 0 dz

(5.366)

Integrating, with the boundary condition CBL CBL,f at z 0, CBL CBL,f m

Z m 冦冤exp(nZ )冥 1冧 usL nusL

(5.367)

where Z is the catalyst bed depth. In terms of the fractional removal of B,

xB

CBL,f CBL CBL,f

m  Z 1  冦冤exp(nZ )冥 1冧   CBL,f  usL nusL CBL,f 

(5.368)

454

5. Catalysis

Gas mixture and first-order kinetics In the previous cases, the gas-phase conservation equation was not needed because the gas was pure A. This is the case where a trickle-bed reactor is used to remove pollutants from a gas stream. In this case, the gas phase is a mixture. Consider the reaction A(gas) bB ; (gas) products (dissolved in liquid) The reaction is catalyzed by the solid phase and the product dissolves in the liquid phase. The intrinsic rate is –rm kmCAS, per unit mass of catalyst. Then (kf au )A 冤CAL CAS 冥 b km sCAS

(5.369)

or CAS

( k f au ) A CAL CAL ( k f a u ) A b s k m

(5.370)

where

( k f au ) A ( k f a u ) A b s k m

(5.371)

For reactant A in the liquid phase (eq. (3.367)), usL

C  dCAL 冢 K L aGL 冣 A  AG CAL  (kf au )A CAL (1 ) 0 dz  HA 

(5.372)

For reactant A in the gas phase (eq. (3.365)),

usG

 usG  dCAG C  dCAG C ( K L aGL )A  AG CAL  0 AG CAL   dz HA  HA   ( K L aGL )A  dz (5.373)

or CAL

 dCAG CAG  usG   H A  ( K L aGL )A  dz

(5.374)

Differentiation of the last equation yields  d 2CAG dCAL 1 dCAG  usG   2 dz H A dz  ( K L aGL )A  dz

(5.375)


455

If eqs. (3.573)–(3.575) are substituted into eq. (3.372), a second-order differential equation is obtained with CAG as the only dependent variable. The expression may be written as d 2CAG dz 2

dCAG CAG 0 dz

(5.376)

where

usG  

usL

u  (  ) (1 )  sG ( H A  k f au A  K L aGL )A   usL usG  ( K L aGL )A  

(kf au )A ( K L aGL )A (1 ) H A usL usG

(5.377)

(5.378)

The boundary conditions are CAG CAG,f (feed) at z 0 and CAL (CAG,f /HA) (equilibrium). Using eq. (5.374), the second boundary condition becomes dCAG /dZ 0 at z 0. The solution of eq. (5.376) under these boundary conditions is CAG 1 冤 m2 exp(m1 Z ) m1exp(m2 Z )冥 CAG,f m2 m1

(5.379)

or

xA 1

CAG CAG,f

(5.380)

where

1 m1 ( 2 4)0.5 2 2

(5.381)

1 m2 冢 2 4冣0.5 2 2

(5.382)

The above solution is also valid in the cases where A is a component of a gas mixture with inerts and B is liquid but its concentration does not change considerably, e.g. in the case where B is a pure liquid.

456

5. Catalysis

Neglecting the change in the concentration of A in the liquid between feed and effluent (CAL ≅ constant), the reaction requires that for each mole of A, b moles of B are removed from the gas stream: b(CAG,f CAG ) (CBG,f CBG )

(5.383)

Then, the fractional removal of B is

xB

CBG,f CBG CBG,f

b

CAG,f CAG,f  C  xA 1 AG  b  CBG,f CBG,f  CAG,f 

(5.384)

This equation can be used for the calculation of the fractional removal of B from the gas only by reaction. This does not account for B that might be removed from the effluent liquid (Smith, 1981). Material balances using an overall rate—Fogler’s approach Mass transfer of the gaseous reactant limiting For a first-order reaction in both A and B, if the first two terms in the denominator of KoA (eq. (3.387)) are dominant or if the liquidphase concentration of B does not vary significantly through the bed (CBS const.),

 1  K Ao Z K AoVR ln  usG QG  1 xA 

M K Ao  s   b QG

(5.385)

In this case, the material balance of B is not needed. The same solution holds for the case of a first-order reaction rate with respect to A without the assumption of constant liquid concentration of B. In the case of variable-volume systems (R ≠ 0) (Fogler, 1999),

 1  K AoVR X (1 R )ln  R A QG,i  1 xA 

M K Ao  s   b QG,i

(5.386)

where QG,i the inlet gas flow rate. In trickle beds, if the gas pressure changes due to pressure drop, then this effect should be taken into account. If the pressure drop of the gas phase is ao in atm/m, then P Pi ao Z

a Z P 1 o Pi Pi

(5.387)


457

Since Ms bVR b ZA

(5.388)

or Z

Ms b A

(5.389)

where A is the cross-sectional area of the bed. Then, in terms of the mass of the solid phase (Fogler 1999), P 1 o Ms Pi o

(5.390)

ao Pi b A

(5.391)

Then, the solution is (Fogler, 1999)  1  K Ao (1 R )ln  X R A bQg,i  1 xA 

o 2    Ms 2 Ms 

(5.392)

 1  K Ao   (1 R )ln  X Z o b AZ 2  R A     2 usG  1 xA 

(5.393)

or in terms of the reactor volume,

Mass transfer and reaction of the liquid species limiting For a first-order reaction in both A and B and provided that the liquid phase is entirely saturated with A throughout the bed, i.e. CAS is constant,  1  K Bo Z K BoVR ln  usL QL  1 xB 

K Bo  

Ms

 b 

(5.394)

QL

Note that the same solution has been found for the case of a first-order reaction rate with respect to B, when the gas-phase concentration of A is constant and the liquid is saturated with A (eq. (5.351)).

458

5. Catalysis

Note that Fogler’s approach is convenient when • • • •

the reaction rate is of first order in both reactants A and B the gas phase is a mixture of A and inerts, and B is a liquid in variable-volume systems (R ≠ 0) when the pressure drop of the gas phase is significant

A typical example of this kind is the hydrogenation of unsaturated organics as presented by Fogler (1999). The reaction is of the form A(g) B(l)C (l) This reaction is of first order in both A and B and the gas phase is a mixture of hydrogen and inerts. Furthermore, the liquid phase is normally pure organic B, and thus it can be assumed that its concentration does not vary significantly through the bed, and thus the case where the mass transfer of the gaseous reactant is limiting is applicable. However, it cannot cover the cases • •

when both reactants are in the gas phase when the liquid concentration of A is changing in the reactor

Example 11 Consider the hydrogenation of an unsaturated organic carried out in a trickle-bed reactor. The reaction is H 2 (g) B(l)C (l) The reaction is of first order in both hydrogen and the organic. However, if the liquid is pure organic and its conversion low, we can assume that its concentration is constant and the reaction rate becomes first order in hydrogen. The gas feed is a mixture of hydrogen and inerts. Suppose that the reaction takes place in a commercial trickle-bed reactor with D 1 m and Z 10 m and that the gas superficial velocity is 0.02 m/s. The feed is H2 and 98% inerts. The liquid mass superficial velocity varies between 2 and 12 kg/m2 s. Use the approach of Smith and Fogler, compare the achieved conversions, and explain the differences. The following data are given by Fogler (1999): T 400 K, P 20 atm, km 3 × 10–5 3 m /kg s, HA 3.81, b 900 kg/m3, l 840 kg/m3, l 0.0018 kg/m s, Dfg 2.4 × 10–9 m2/s, 0.4 and s 0.28. Since the reaction is of first order with respect to hydrogen for 50% inerts, it is reasonable to assume that for even lower hydrogen concentration (98% inerts) the reaction order does not change.


459

Solution With the specified hydrogen concentration of 2%, the expansion factor is –0.02 and thus it can be assumed to be approximately zero. To use the models, the next step is to find the pressure drop in the reactor. This is vital because if the pressure drop is significant, then its effect on the operation should be taken into account. In order to evaluate the pressure drop in the reactor, we can use the Ergun equation for both phases and then the two-phase pressure drop can be evaluated by using the Larkins equation (eq. (3.390)). For the determination of pressure drop of the gas phase, we need the viscosity and the density of the gas stream. Assuming that the inert phase is N2 or a similar gas, we can take l 17.07 kg/m3, l 2.21 × 105 kg/m s . The resulting gas (0.357 kg/m2 s) and liquid mass superficial velocities (2–12 kg/m2 s) are within the operating region for commercial trickle beds (see Figure 3.47 and Satterfield, 1975). The gas-phase pressure drop is better evaluated by using the differential form of Ergun’s equation and taking into account the gas density variance. However, it is easy to show that the parameter aZ/Pi is about 0.001, and thus the Ergun equation in its classic form can be accurately used (see subsection Nonisobaric fixed-bed operation). Then, the total pressure drop for the maximum mass superficial velocity of liquid is, for the single phases, 0.02 atm for the gas and 0.68 atm for the liquid, and for the two-phase system 1.43 atm. Thus, we can assume that the pressure-drop effect is minimal. Under the reasonable assumptions of zero expansion and negligible pressure drop, the model of Smith for gas mixture eq. (5.379) is also applicable. The gas–liquid mass transfer for organic solutions and the liquid–solid mass transfer are evaluated using the appropriate correlations (eqs. (3.427) and (3.435), respectively), while the Fogler’s overall coefficient (KoA) is (eq. (3.379)) K Ao K o

1H 1 1 1 ( K L aGL )A (kf au )A b km

The results are shown in Table 5.13. Using the Smith and Fogler models (eqs. (5.379) and (5.385) respectively), we obtain the results shown in Figure 5.15. It is evident that the models result in close though slightly different hydrogen conversions. The model of Smith results in lower conversions and the difference between the Table 5.13 The values of the various coefficients GL (kg/m2 s)

kfgaGL (s–1)

kf au (s–1)

KoA (s–1)

2 5 8 12

1.72 103 2.17 103 2.44 103 2.70 103

1.39 102 3.98 102 6.83 102 10.9 102

3.34 104 4.23 104 4.70 104 5.11 104

460

5. Catalysis

0.25 0.20

X

0.15 0.10 0.05

X (Fogler) X (Smith)

0.00 0

2

4

Figure 5.15

6 8 GL (kg/m2*s)

10

12

14

The conversion versus GL.

models is higher for a higher mass superficial velocity, i.e. higher liquid superficial velocity, reaching the maximum value of 15%. This result illustrates the effect of Fogler’s assumption of zero liquid superficial velocity, analyzed in Section 3.7.2 and eq. (3.374). Indeed, for a low liquid superficial velocity, the models result in almost the same values. Thus, it can be stated that Smiths approach is more accurate for high liquid superficial velocities. However, Fogler’s approach is more useful and accurate in the case of considerable pressure drop and gasphase expansion. One more important parameter is the wetting of the catalyst, which for the simplified models is assumed to be 100%. Using the Al-Dahhan et al. (eq. (3.414)) correlation for high pressures, the results shown in Table 5.14 are obtained. This result indicates the need for an efficient distributor design for this trickle-bed operation at low flow rates (GL < 12). Trickle-bed models assume plug flow for both phases. Thus, it is interesting to evaluate the respective Peclet numbers. The correlations of Michell–Furzer for liquid (eq. (3.417)) and Hochman–Effron for gas (eq. (3.419)) are used and the results are shown in Table 5.15. The Reynolds number for the gas phase is 32.28. Taking into account that the ratio Z/dp is 5000, we can assume that the plug-flow condition is assured for both phases. Here, we can use the criterion of Mears (eq. (3.421)): Z 20 n  Cf  ln dp PeL  Co  where Cf and Co are the feed and outlet concentrations, respectively. This criterion gives the minimum Z/dp ratio required to hold the reactor length within 5% of that needed for plug flow. In our case, the limit of this ratio is only 5.63–6. Thus, this criterion is satisfied as well.


461

Table 5.14 fw versus GL GL (kg/m2 s)

fw

2 5 8 12

0.51 0.71 0.85 0.99

Table 5.15 Reynolds and Peclet numbers GL (kg/m2 s)

ReL

PeL

PeG

2 5 8 12

2.62 5.55 8.89 13.34

0.54 0.60 0.65 0.68

0.15 0.15 0.14 0.14

Example 12 Wu et al. (1996) studied the liquid-phase decomposition of hydrogen peroxide in a trickle bed of 2.2 cm diameter and 30 cm height, at 22 °C and 1 atm: H 2 O2 (l)H 2 O (l) 1 2 O2 (g) The inlet concentration of H2O2 is 1.18% (w/w), the space-time based on liquid flow is between 60 and 300 s and the gas superficial velocity is 5.5 cm/s. The catalyst is copper chromite on alumina support with the following characteristics: dp,avr 1.159 mm, p 1.05 g/cm3, Ss 120 m2/g, and Vp 0.36 cm3/g. The reaction is first order with respect to hydrogen peroxide and the effectiveness factor s is found to be equal to 0.24. This effectiveness factor accounts only for internal transport effects. Due to the dilute feed of hydrogen peroxide, the operation can be considered isothermal. In Table 5.16, some of the experimental results for the runs in the trickle-bed reactor are presented (approximate values). • •

• •

Evaluate the intrinsic reaction coefficient by using the appropriate kinetic data. Using the evaluated intrinsic reaction coefficient, compare the predictions made by using the simple trickle-bed reactor model for this type of reaction. Explain the differences. Evaluate the tortuosity of the catalyst support. What is the expected conversion for different particle size and space-time of 60 s?

462

5. Catalysis

Table 5.16 Experimental results (s)

x

60 100 200 300

0.44 0.60 0.80 0.85

Solution First, we have to check the wetting efficiency of the bed because the simple trickle-bed model assumes complete wetting and thus it is not applicable otherwise. By using the El-Hisnawi et al. correlation, the wetting efficiency can be estimated (eq. (3.411)). To do that, we need the Galileo number and the Reynolds number. The liquid superficial velocity can be evaluated as follows:

uL,s

Z

while the Galileo number is 4.83 × 104 (eq. (3.412)). The results are summarized in Table 5.17. At this point, it is also important to check the plug-flow assumption, as the models assume plug flow for liquid. The correlation of Michell–Furzer (eq. (3.417)) for the liquid is used and the results are shown in Table 5.17. The minimum values of Z/dp are evaluated by using the Mears criterion (eq. (3.421)). The value of Z/dp used in the experiments is 188.98, and thus the plug-flow assumption is valid in the whole region of working residence time. Furthermore, from the point of view of catalyst wetting, it is clear that the appropriate experimental point that we can use is the one of 60 s space-time where x 0.44 and fw 1. Then, from eq. (5.351),  1  koZ k o 9.67103 s1 ln   1 xB  usL where 1 1 1 k o ( k f a u ) B b s k m


463

Table 5.17 Hydraulic parameters and Mears criterion for plug flow

(s)

ReL

fw

PeL

(Z/dp)min

60 100 200 300

8.80 5.28 2.64 1.76

1.03 0.96 0.87 0.82

0.64 0.59 0.54 0.51

18.22 30.12 59.85 74.67

Then, to evaluate the intrinsic reaction coefficient, we need the mass transfer coefficient in the liquid film, which can be calculated by means of the correlation of Dharwadkar and Sylvester (fw 1) (eq. (3.433)): 0.331  L Df  kf au 1.637au usL Re L   L

23

Assuming that the bed porosity is 0.4, the specific area au is (eq. (3.447)) au

6(1 ) 2267.72 m 2 m 3 dp

The next parameter we need is the diffusion coefficient Df of hydrogen peroxide in water. Here, we can assume the approximate value of 10–9 m2/s. However, this coefficient will be needed further in this example for the determination of the effective solid-phase diffusion coefficient, in a calculation that is extremely sensitive to the value of the liquid-phase diffusion coefficient. For this reason, coefficient should be evaluated with as much accuracy as possible. The diffusion coefficient of solutes in dilute aqueous solutions can be evaluated using the Hayduk and Laudie equation (see eq. (I.26) in Appendix I): Dw

8.6211014 0.589 1.14 w VGB

For this, we need the molar volume VGB of hydrogen peroxide at normal boiling point, which can be evaluated using the following correlation (see eq. (I.26) in Appendix I): VGB 0.285 VC1.048 where VC is the molar volume of hydrogen peroxide at critical conditions, which is 77.7 cm3/mol (see Table I.9 in Appendix I) and then VGB 27.29 cm3/mol. The resulting diffusion coefficient is 2.13 × 10–9 m2/s. Note that SI units have to be used with the Hayduk

464

5. Catalysis

and Laudie correlation, while CGS units have been used in the correlation for the molar volume at normal boiling point. Then, the resulting mass transfer coefficient is 0.16 s–1. Furthermore, the bulk density of the bed is b (1 ) p 630 kgm 3 Then 1 1 1 km 6.81105 m 3 kg s b s k m k ( k f a u ) B The comparison of the experimental and model conversions is shown in Figure 5.16. It is evident that the model predictions are very close to the experimental values for a high wetting efficiency, while the model predicts higher conversions for lower wetting efficiencies. This is expected as the simple model assumes complete wetting, i.e. better performance of the reactor. For a first-order reaction and isothermal operation, the effectiveness factor is (eq. (5.77)) s

1 1 1  tanh(3) 3 

Applying a trial-and-error procedure, the Thiele modulus is estimated as 3.80. Assuming spherical particles, the Thiele modulus is (eq. (5.76))

rp

km p

3

Deff

1.0 0.8

X

0.6 0.4 Xmod Xexp

0.2

fw 0.0 0

50

100

150

200

250

300

350

τ (s)

Figure 5.16

The comparison of the experimental and model conversions.


465

Using this expression, the effective solid-phase diffusion coefficient is found to be equal to 3.46 × 10–10 m2/s. This coefficient is related to the liquid-phase coefficient as follows (eq. (3.602)): Deff

p Df p

In order to evaluate the tortuosity of the catalyst, we need its porosity. From the data given, p Vp p 0.38 So, the value of tortuosity is 2.33, within the expected limits for alumina, i.e. between 2 and 6. By changing the particle size, the effectiveness factor, liquid mass transfer coefficient, and wetting efficiency are affected. We choose such particle sizes to have complete wetting and thus be able to use the simple model. For the specified space velocity, the particle size should be lower than about 2.5 mm. On the other hand, particles smaller than 0.5 should be avoided for preventing a high pressure drop appearance. Furthermore, the values of Z/dp for 0.5 < dp < 2.5 mm are between 120 and 600 assuring plug-flow conditions. The results are shown in Figure 5.17. The benefit from using lower particle sizes as well as the strong effect of particle size is clear. For example, by increasing the space-time from 60 to 100 s (66.67% increase), the conversion is increased by 38.64%, while by decreasing the particle size by the same percentage, i.e. from 1.59 to 0.53 mm, the conversion is increased by 72.73%, which is almost double. A few comments: Hydrogen peroxide is an odorless and colorless liquid used as a source of hydroxyl radicals in environmental applications. There is no conclusive evidence for the carcinogenicity of hydrogen peroxide to humans. However, it leads to genetic damage in vitro through the formation of free radicals, whereas acute lung damage and edema has been reported as the respiratory effect of hydrogen peroxide (Cralley and Cralley, 1985). Example 13 Consider the catalytic oxidation of ethanol: C2 H 5 OH (l) 3 O2 (g)2 CO2 (g) 3 H 2 O (l) This reaction is catalyzed by a hydrophilic spherical Pt--Al2O3 catalyst of 3 mm particle size at 70 °C and 1 atm and is considered to be first order with respect to oxygen and 0.5 order with respect to ethanol (Horowitz et al., 1999). The effective rate coefficient, i.e. the rate coefficient including the effect of the internal diffusion is 1.95 × 104 (m3/kg s) (m3/kmol)0.5.

466

5. Catalysis

1.0 ηs

X

0.8 0.6 0.4 0.2 0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

d p (mm)

Figure 5.17

Conversion and effectiveness factor versus particle size.

The reaction takes place in a trickle bed of 4.1 cm diameter and 1 m height with liquid flow rate of 2.17 kg/s m2, gas flow rate of 0.017 kg/s m2, and inlet ethanol concentration of 0.006 kmol/m3. Horowitz et al. (1999) noted that pure oxygen was used as the gas feed, and under the experimental conditions the conversion of O2 was less than 5%. Thus, the gas-phase concentration of O2 can be considered constant. Furthermore, the liquid phase entered the reactor saturated with O2. Under these conditions, the conversion of the ethanol was about 14%. Estimate the height of the bed to achieve the same performance of the reactor by using the appropriate simplified model, assuming that the liquid phase remains saturated with O2 throughout the reactor length, plug-flow conditions exist, and the external wetting of the catalyst particle is complete. Assume that the particle density of alumina is 1050 kg/m3 and the bed porosity is 0.45. Solution The intrinsic reaction rate is 0.5 (rm ) km,eff CO2 ,SCEtOH,S

Then, the oxygen balance around the catalyst particles is (eq. (3.369)) 0.5 (kf au )A 冤CO2 ,L CO2 ,S 冥 b km,eff CO2 ,SCEtOH,S

or CO2 ,S

( k f au ) A 0.5 (kf au )A b km,eff CEtOH,S

 CO2 ,G   H  A


467

then

(kf au )B 冤CEtOH,L CEtOH,S 冥

0.5 b km,eff CO2 ,SCEtOH,S

a

k  CO2 ,G  a  H A 

In analogy to eq. (5.341), the coefficient ko is defined as

k

0.5 (kf au )A b km,eff CEtOH,S 0.5 (kf au )A b km,eff CEtOH,S

1 1 1 0.5 k (kf au )A b km,eff CEtOH,S

The second term of ko in the above equation is a function of the surface ethanol concentration, which is unknown. However, its maximum value cannot be higher than the feed ethanol concentration and under the specific operating conditions, 1 13.52 s ( k f au ) A   1 1 114.64 s   0.5 0.5  b km,eff CEtOH,S  min b km,eff CEtOH,f

1 128.16 s k

The mass transfer term of ko is about 10.6% of ko. Considering that the second term will be even higher due to the lower surface concentration of ethanol during the reaction evolution, the contribution of the mass transfer term to ko can be considered minimal. This approximation, though rough, greatly simplifies the problem solution. Then 0.5 k ⬵ b km,eff CEtOH,S

The ethanol balance around the catalyst particles is (eq. (3.370))

(kf au )B 冤CEtOH,L CEtOH,S 冥

0.5 b km,eff CEtOH,S  CO2 ,G   H  a A

468

5. Catalysis

The above equation becomes 2 2 2CEtOH,S (2 2 2CEtOH,L ) CEtOH,S 2CEtOH,L 0

( k f au ) B

b km,eff  CO2 ,G  a  H  A

Rearranging the trionym, 2 2 CEtOH,S CEtOH,S CEtOH,L 0

  2  2 2CEtOH,L    It is easy to show that for the specified conditions (x 0.14), 2 1.78 107 2 (2CEtOH,L )min 2CEtOH,f (1 x ) 1.03 102 and thus ⬵ 2CEtOH,L The trionym becomes 2 2 CEtOH,S 2CEtOH,L CEtOH,S CEtOH,L 0

(CEtOH,S CEtOH,L )2 0 CEtOH,S CEtOH,L


469

This is a result of the assumption that the mass transfer coefficient is minimal. Then, using the mass conservation equation for ethanol in the liquid phase (eq. (3.368)),

usL

d CEtOH,L

usL

dz

(kf au )B 冤CEtOH,L CEtOH,S 冥 0

d CEtOH,L dz

0.5 b keff CEtOH,L  CO2 ,G   H  0 a A

The boundary (feed) condition is CEtOH,L CEtOHL,f at z 0. Then, on integration,  0.5 k Z  CO ,G   CEtOH,L CEtOH,f 0.5 b eff  2   ausL  H A   

2

For 14% conversion, CEtOH, L 0.00516 kmol/m3 and Z 0.795 m. The difference from the real value (1m) is mainly due to the approximation made about the mass transfer coefficient as well as the complete wetting of the catalyst, as the actual wetting efficiency is 88%. Furthermore, the problem is more complicated because under incomplete wetting, the gas reactant reaches the catalyst surface more easily than the unwetted part, as Horowitz et al. found out experimentally. The aim of this example is to demonstrate the use of the simplified model for reactions other than first order with respect to the gas reactant and zero order to the liquid one, and more specifically to demonstrate the case of first order with respect to the gas reactant and half order to the liquid one, which may have, under specific operating conditions, an analytic solution. For example, if the liquid mass superficial velocity was higher, say 10 kg/m2 s, the wetting efficiency is 1 and the mass transfer contribution lower than 4.07%. At the same time, there is no contribution of the unwetted part of the catalyst. Under these conditions, the approximate model is expected to exhibit a better performance. The same result can be achieved for smaller particles. Example 14 Consider the catalytic oxidation of phenol: C6 H 5 OH (l) 7O2 (g) 6CO2 (g) 3H2 O (l) This reaction is catalysed by a Pt–Ru catalyst with activated carbon support at 35°C and it is considered to be pseudo-first order with respect to phenol under the condition of O2 excess (Atwater et al., 1997). The reaction was studied in a trickle-bed microreactor of 1.27 cm diameter and volume of 4.8 cm3. The bed porosity is 0.28, while the ratio of bed length-to-particle size is about 30. The reactor was operated under pressure enough to keep a single-phase flow, while the liquid feed was saturated with oxygen. Under these conditions, we can assume complete wetting of the catalyst particles.

470

5. Catalysis

The feed phenol concentration is 0.16 mM (15 mg/L) and the contact time based on superficial velocity is 4.3 s. The exit concentration under the specified conditions is about 0.10 mM. Assume that the bulk density of the carbon catalyst is 800 kg/m3. • • •

Evaluate the effective rate coefficient, i.e. the product of internal effectiveness factor and intrinsic rate coefficient for this reaction Evaluate the time needed to achieve the same conversion in a packed bubble bed reactor of the same dimensions under complete recycle of the liquid phase with VL/VR 2. What is the volume of liquid that the trickle bed can treat if it is operated for the same time?

Solution Trickle-bed reactor: First, we have to evaluate the bed length and the particle size used. The bed length is Z

VR VR 3.79 cm A ( D2 4)

The particle size is dp

30 0.126 cm Z

For the specified particle size and bed porosity, the specific area au is au

6(1 ) 3418.54 m 2 m 3 dp

Furthermore, for the specified contact time, the superficial velocity is usL

Z 0.88 cms

which corresponds to a Reynolds number of 12.36. As the reactor was operated under pressure enough to keep a single-phase flow while the liquid feed was saturated with oxygen, the situation is equivalent to constant gas-phase concentration and saturated liquid phase. Thus, the model solution appropriate for a firstorder reaction with respect to the liquid reactant is (eq. (5.350))  1  k Z ln   1 xB  usL


471

where 1 1 1 k ( k f a u ) B b s k m For the specified exit concentration, xB 0.375 and thus ko 0.109 s1. In order to evaluate the effective reaction coefficient, we need the mass transfer coefficient in the liquid film, which can be calculated by using the correlation of Dharwadkar and Sylvester (fw 1) (eq. (3.433)): 0.331  L Df  kf au 1.637au usL Re L   L

23

The next parameter required is the diffusion coefficient of phenol in water (Dfg). Here, we can assume the typical value of 10–9 m2/s. Then, the resulting mass transfer coefficient is 0.23 s–1. Subsequently, 1 1 1 s km 2.6 104 m 3 kg s b s k m k ( k f a u ) B At this point, it is also important to check the plug-flow assumption as the simple model assumes plug flow for the liquid. For the specified Reynolds number, the Peclet number is found to be 0.59 using the Michell–Furzer correlation (eq. 3.417). The minimum value of Z/dp, evaluated using the Mears criterion, is 15.87 (eq. (3.421)), lower than the value used in the experiments, which is about 30. Thus, the operation can be assumed as plug flow. Packed bubble bed reactor with complete recycle: The model solution for the specific reactor and constant gas-phase concentration is (eq. (5.151)) t

VL 1  1  ln VR k  1 xB 

where 1 1 1 k b s k m ( k f a u ) B and VL/VR 2. In order to evaluate the coefficient ko, the mass transfer coefficient in the liquid film is needed, which can be calculated from the Mochizuki–Matsui correlation (fw 1, ReL > 5) (eq. (3.438)): kf dpe Dfg

 usL L dpe  0.75    L

0.5

 L     L Dfg 

0.33

(1 4)

472

5. Catalysis

Due to the absence of gas flow, the parameter is zero. Note that CGS units are used with this correlation. Furthermore (eq. (3.437)), 2 0.033 cm dpe dp 3 1 The resulting mass transfer coefficient is 3.87 × 103 cm/s, and thus kf au 0.132 s1 and the coefficient ko is equal to 0.081 s1 and the time needed for achieving the conversion level of 0.375 in this reactor mode is 11.62 s. The space velocity of the trickle-bed reactor is 1 s 0.23 s1 which means that it can treat 0.23 bed volumes per second. Then, if the trickle bed were operated for the same time as the packed bubble bed reactor, the treated volume would be 2.7 times its volume, or VL/VR 2.7. From this point of view, the trickle-bed reactor is preferred. At this point it, has to be noted that the use of recycle reactors is very attractive, in commercial as well as laboratory applications. Specifically, they can be operated for long periods, thus leading to high conversions, in cases where the per-pass conversion of the liquid component is small. Moreover, they present some of the benefits of continuous operations, though being batch, such as easy filling and removing of the catalyst (Ramachandran and Chaudhari, 1984). In the laboratory, recycle reactors are suitable for kinetic studies of catalytic reactions (Smith, 1981). By adjusting the recycle rate, i.e. the flow rate, the reactor operates in differential mode. A few comments on phenol: Phenol is a monosubstituted aromatic hydrocarbon. In its pure state, it exists as a colorless or white solid. This pure compound is mixed with water and commercially sold as a liquid product. Phenol gives off a sweet, acrid smell detectable to most people at 40 ppb in air and at about 1–8 ppm in water (EPA, 2002). It evaporates more slowly than water and is moderately soluble in water. Phenol is also combustible. Phenol is produced through both natural and anthropogenic processes. It is naturally occurring in some foods, human and animal wastes, and decomposing organic material, and is produced endogenously in the gut from the metabolism of aromatic amino acids. Phenol has been isolated from coal tar, but it is now synthetically manufactured (EPA, 2002). Currently, the largest use of phenol is as an intermediate in the production of phenolic resins, which are used in the plywood, adhesive, construction, automotive, and appliance industries. Phenol is also used in the production of synthetic fibers such as nylon and for epoxy resin precursors such as bisphenol-A.


473

Phenol is toxic to bacteria and fungi, and it is used as a slimicide and disinfectant. Because of its anesthetic effects, phenol is used in medicines such as ointments, ear and nose drops, cold sore lotions, throat lozenges and sprays (such as those sold under the Cepastat® and Chloraseptic® labels), and antiseptic lotions (EPA, 2002). Phenol does not appear to accumulate significantly in the body. Several epidemiological studies have evaluated the carcinogenicity of phenol, but they have not found a consistent dose-related association (EPA, 2002). Because all the subjects were also exposed to other chemicals and there was no correction for smoking, these studies are not adequate to reach conclusions on the carcinogenic potential of phenol. A number of toxic effects secondary to decreased water consumption have been observed, including decreased body weight compared to controls, decreased pup weight, and decreased pup survival, preculling. Other effects that may not have been secondary to decreased water consumption were kidney inflammation and decreased motor activity (EPA, 2002). Gavage studies found more severe effects and reported these effects at lower doses. Observed effects included lung, liver, and kidney pathology; tremors and other nervous-system effects; and at sufficiently high doses, death. Example 15 Medeiros et al. (2001) studied the catalytic oxidation of sulfur dioxide on active carbon particles in a trickle-bed reactor at 25 °C and 1.1 atm: 1 SO2 (g) O2 (g) H 2 O (l) H 2 SO 4 (l) 2 The size of catalyst particles is 2.5 mm, packed in a fixed bed of 5 cm diameter, 25 cm height, and porosity of 0.4. The gas is a mixture of sulfur dioxide and air with a flow rate of 1.515 × 103 m3/s and feed concentration of SO2 0.0777 mol/m3. For such low sulfur concentration, the inlet oxygen concentration can be considered approximately equal to its concentration in air. For the same reason, the expansion factor of the gas mixture can be taken as zero. Under these conditions, the reaction rate is first order with respect to sulfur dioxide and the rate coefficient per unit volume of catalyst, including the effect of internal diffusion, is equal to kvs 0.3 m3/m3 s. According to the experimental results of Medeiros et al. (2001), the wetting efficiency is 100% and plug flow is assumed for liquid flow rates in the vicinity of 14 × 105 m3/s, which is the case in this example. Furthermore, a liquid-phase distributor is used on the top of the bed and the overall gasphase mass transfer coefficient was experimentally measured as 0.153 s1 for liquid flow rate equal to 14 × 105 m3/s. Under these conditions, the experimental value of sulfur dioxide conversion was approximately 18%. • •

Evaluate the theoretical conversion of sulfur dioxide by using the model derived for gas mixture in trickle beds. Check if the assumptions of the model are met. Evaluate the conversion of sulfur dioxide in the trickle flow regime by proposing an appropriate gas and liquid flow rate.

474

5. Catalysis

Solution Before applying any model, we should first check if the assumptions of the model are satisfied by the experimental conditions. If not, the model results are questionable. The experiments for the specified study have been made in the pulsing flow regime. This is stated in the original article, and it is evident by calculating the liquid and gas mass superficial velocities, which are GL usL L 71.19 kgm 2 s GG usG G 0.93 kgm 2 s By using the approximate Figure 3.47, it is clear that we are by far away from the trickling region and into the pulsing-flow regime. However, this does not mean that we cannot use the models derived for the trickle-bed regime as long as (a) (b) (c) (d) (e)

we use the appropriate mass transfer coefficients, especially the gas–liquid one it is sure that the wetting efficiency of the catalyst particles is near to 100% it is assured that plug-flow conditions exist, especially in the liquid phase the expansion of the gas mixture is approximately zero it is assured that the pressure drop is relatively low in comparison to the inlet pressure of the gas.

The conditions (a), (b), (c), and (d) are met since we have the experimental value of the gas–liquid mass transfer coefficient, the wetting efficiency is given to be 100%, plug-flow condition is assumed in the original study, and the expansion factor is zero as the oxygen concentration has been taken as constant. The only problem with the application of the simplified model is the pressure drop in the trickle bed. By using the Ergun equation (3.449), the pressure drop in the liquid phase is very high, approaching the value of 42 kPa/m, while the gas-phase pressure drop is approximately 1.9 kPa/m. Using the correlation of Larkins et al. (eq. (3.390)), the twophase pressure drop is almost 103 kPa/m, which is equal to the inlet pressure drop of the gas mixture. Here, we have to note that it becomes so high because of the high pressure drop in the liquid phase. Furthermore, the Larkins et al. correlation holds for the tricklingflow regime, and thus the above calculations are rough. However, it seems that the pressure drop is quite high, due to the very high liquid-phase flow rate. Basic parameters evaluation: Before proceeding to the application of models, some basic parameters are needed. The first and most important one is the rate coefficient of the reaction. The rate coefficient should be expressed in terms of the catalyst mass (eq. (3.10)): km

kvs 3.75 104 m 3 kg s p

Note, that this coefficient is the effective one as it includes the contribution of the intraparticle diffusion.


475

The second parameter is the Henry constant for sulfur dioxide, which can be found in Table I.18, Appendix I, and is 1.23 mol/L atm. The Henry constant has to be expressed in its dimensionless form, which is (see eq. (I.35) in Appendix I) 1

  mol   1.23    atm L   H  0.0333  atm L   atm L  24.44 0.082  298 K   mol   mol K   atm L  H   mol 

The inlet oxygen concentration is evaluated by using the ideal gas law:

CBG,i

P RT

1.1 atm  atm m 3  82.057  298 K  mol K 

9.38 106 molcm 3

The liquid-phase diffusion coefficient of sulfur dioxide can be found from Table I.10, Appendix I, and is equal to 1.7 × 109 m2/s. Finally, note that the material balances will be written for sulfur dioxide and thus b 0.5. Model application in the pulsing-flow regime: The mass transfer coefficient in the liquid– solid film is evaluated by means of the Dharwadkar and Sylvester correlation (eq.3.433), and is found to be 0.45 s1. Then, the several parameters of the model eq. (5.379) are shown in Table 5.18. The model conversion is 16.3%, which is very close to the experimental value. Model application in the trickle-flow regime: In order to assure operation in the trickleflow regime, the gas as well as the liquid flow rate has to be considerably lowered. At the same time, the conditions (a) and (d) are met in the reactor, while the rest of the conditions have to be checked. Operating conditions: Following Figure 3.47 in order to operate in the trickle-flow regime, the gas and especially the liquid flow rate should be lowered. We choose a liquid

Table 5.18 Model parameters Parameter

m1 m2

Value 0.71 9.89 10.7 1.24 8.66

476

5. Catalysis

and gas flow rate of 9.33 × 10–6 m3/s and 7.58 × 10–4 m3/s, respectively, which leads to the following mass superficial velocities: GL usL L 4.75 kgm 2 s GL usG G = 0.46 kgm 2 s Wetting efficiency: The Reynolds and Galileo numbers are 13.18 and 1.88 × 105. Using the correlation of El-Hisnawi (eq. (3.411)), the wetting efficiency is 0.99, satisfying condition (b). Pressure drop: Using the Ergun equation, the liquid and the gas pressure drop are 0.726 and 2.12 kPa/m, respectively. Then, by using the correlation of Larkins et al., the two-phase pressure drop is equal to 10.8 kPa/m, ten times lower than in the pulsing-flow regime and low enough to assure that the gas density is almost constant. Thus, the condition (e) is satisfied. Plug-flow assumption: Using the Michell and Furzer correlation (eq. (3.417)), the liquidphase Peclet value is 0.66 and the minimum value of Z/dp, evaluated by using the Mears criterion (eq. (3.421)) 4.81, which is much lower that the experimental one, which is 100. Then, condition (c) is satisfied. Results: The mass transfer coefficient in the liquid–solid film is evaluated using the Dharwadkar and Sylvester correlation (eq. (3.433)), and is found to be 0.073 s–1. The mass transfer coefficient in the gas–liquid film is evaluated by means of the Goto and Smith correlation (eq. (3.426)), and is equal to 1.49 × 10–2 s–1. It is evident that the mass transfer coefficients are much lower than the coefficients in the pulsing-flow regime. Then, the several parameters of the model eq. (5.379) are shown in Table 5.19. The model conversion is 14.7%, which is close to the value calculated for the pulsingflow regime. The oxygen conversion is very low, as assumed, about 0.06 %. Although the mass transfer coefficients are much higher in the pulsing-flow regime, the contact time is much lower due to the higher liquid and gas flow rates, and thus these two effects are “balanced,” resulting in a good conversion in the trickle-flow regime. Example 16 Consider the catalytic oxidation of ethanol: C2 H 5 OH (l) O2 (g) 2 C (s) 3 H 2 O Table 5.19 Model parameters Parameter

m1 m2

Value 0.29 15.20 12.66 0.88 14.32


477

This reaction is catalyzed by Pd–Al2O3 catalyst at 30 °C and is considered to be first order with respect to oxygen (Hopper et al., 2001; Ramachandran, and Chaudhari, 1980). The rate coefficient for this reaction is km 0.0177 cm3/g s. The reaction takes place in a trickle-bed reactor of 20 cm diameter and 500 cm height and in a packed bubble bed reactor of the same dimensions. Compare these two reactors under the following conditions: • •

constant gas-phase concentration and saturated liquid phase with oxygen constant gas-phase concentration and zero inlet liquid-phase concentration of oxygen.

The gas superficial velocity is 0.36 cm/s, the liquid superficial velocity is 1.06 cm/s, the ethanol inlet concentration is 4 10–4 mol/cm3, the oxygen pressure 35.5 atm, the catalyst particle size 2 mm, the particle density 1800 kg/m3, the bed voidage 0.42, the tortuosity of alumina 3, and its particle porosity 0.4. Solution Due to the high pressure, it is important to evaluate the properties of the gas phase, i.e. of oxygen. Here, we don’t present the calculations but • •

the density is evaluated from calculations based on the ideal gas law, the viscosity is evaluated using the Jossi et al. correlation (see eq. (I.12) in Appendix I)

The correlations as well as the parameters needed for these calculations are given in Appendix I. The resulting density is 45.72 kg/m3 and viscosity 2.48 × 105 Pa s. Hydraulic analysis: a. Trickle-bed reactor: The pressure drop in the trickle-bed reactor is evaluated using the correlation of Larkins et al. (eq. (3.390)). However, at first we have to evaluate the singlephase pressure drop for the gas and the liquid. Using the Ergun equation (3.449), the pressure drop is 2380.74 and 19.10 Pa/m for the liquid and the gas phase, respectively. Then (eq. (3.391)  ( PL Z )  X   ( PG Z ) 

0.5

11.16

and thus (eq. (3.390))   5.0784 ( PGL Z ) 冤( PL Z ) ( PG Z )冥exp  2  3.531 (ln X )  ⬵ 4131 Pam

478

5. Catalysis

Due to the relatively high pressure, the wetting efficiency is evaluated from the correlation of Al-Dahhan et al. (eq. (3.414)): 1 9

  P     Z   1  L g         1 3  fw 1.104 ReL   Ga L          

0.92

where ReL 23.42 and GaL 9.65 × 104. It is evident that the wetting efficiency is high enough to assume complete wetting, and thus to use the simplified reactor models. The next parameter of importance is the Peclet number of the liquid and the gas phase. For the specified Reynolds number, the Peclet number for the liquid phase using the Michell–Furzer correlation (eq. (3.417)) is 0.74. The minimum value of Z/dp for ethanol conversion between 0.1 and 0.9, evaluated using the Mears criterion (eq. (3.421)), is 2.84 and 62.11 respectively, much lower than the value used in the example, which is about 2500. Thus, the operation can be assumed to follow the plug-flow model. Packed bubble bed reactor: The pressure drop in the packed bubble bed reactor is evaluated using the Huntington correlation (eq. (3.395)). The parameters needed are the following (eqs. 3.396, 3.397, and 3.398): 2 0.25 cm dpe dp 3 1  usG G dpe  1.167    0.9   G    w  339.61  0.767      usL L dpe    L     L exp 冦8 1.12 ln () 0.0769 冤 ln()冥2 0.0152冤 ln()冥3 冧 6.49 and thus (eq. (3.395)) 2 PGL 2usG G 245.71 dynm 3 ⬵ 2457 Pam Z dpe

In this correlation, CGS units are used. It can be seen that the pressure drop in this reactor is lower than that in the trickle-bed reactor, under the same operating conditions.


479

Concerning packed bubble bed reactors, the evaluation of the Peclet number of the liquid phase is important in order to decide if we have to use a plug- or backmixed-flow model. For the specified Reynolds number, the Peclet number for the liquid phase using the Stiegel–Shah correlation (eq. (3.422)) is 0.15, much lower than in the trickle bed, which was expected as the backmixing in the liquid phase in packed bubble bed reactors is relatively high. The liquid phase can be considered to be well mixed if (Ramachandran, and Chaudhari, 1980) (eq. (3.423)) PeL

Z 4 dp

In our case the right hand parameter of this criterion is about 373, which means that we can use the plug-flow model. Note that we are less interested in the mixing of the gas phase. This explains why the gas-phase concentration is considered to be constant, and thus its material balance is not involved in the model. Mass transfer coefficients: In the mass transfer calculations, we need the Henry constant of oxygen in water at 30 °C, which can be evaluated using the relevant correlations presented in Section I.3.2, Appendix I, and is equal to 34.03. The next parameter we need is the diffusion coefficient of oxygen in water at 30 °C, which can be also found in Table I.10, Appendix I. The correction for the temperature has been also presented in eq. (I.28) Appendix I. The evaluated diffusion coefficient is 2.5 10–9 m2/s. a.

b.

Trickle bed reactor: Using the Goto and Smith correlation (eq. 3.426), the gas–liquid mass transfer is found to be 2.5 10–2 s–1. The liquid–solid mass transfer is evaluated using the Dharwadkar–Sylvester correlation (eq. (3.433)) with au 1740 m–1 and is equal to 0.21 s–1. Packed bubble bed reactor: Using the Reiss correlation (eq. (3.432)), the gas–liquid mass transfer is found to be 9.02 10–2 s–1. The liquid–solid mass transfer is evaluated from the Mochizuki–Matsui correlation (eq. (3.436)) for ReL 5 and is equal to 0.24 s–1. The parameter is equal to 0.51 (eq. (3.439)), while (eq. (3.437)) 2 0.097 cm dpe dp 3 1

In the Mochizuki–Matsui correlation, CGS units should be used. Note that the mass transfer coefficients are higher in the packed bubble bed reactor compared to those in the trickle-bed reactor under the same conditions, as expected. The intraparticle phenomena: The next step is the evaluation of the internal effectiveness factor. The unknown parameter is the effective solid-phase diffusion coefficient, which is (eq. (3.602)) Deff

p Df p

3.36 1010 m 2 s

480

5. Catalysis

Assuming spherical particles, the Thiele modulus is (eq. (5.76))

rp

km p

3

Deff

3.25

For a first-order reaction and isothermal operation, the effectiveness factor is (eq. (5.77))

s

1 1 1  ⬵ 0.28   tanh (3) 3 

The application of the models: The gas-phase concentration of oxygen, which is needed for the application of the models, is evaluated from the ideal gas law and is 1.43 10–3 mol/cm3. a. Trickle-bed reactor: The coefficient ko is 1 1 1 k 4.99 103 s1 k ( k f a u ) A b s k m Then, for zero inlet liquid-phase concentration of oxygen, we use eq. (5.368), where m 0.175 and n 2.84 (eqs. (5.364) and (5.365)), and thus the conversion is 20.7%. For liquid saturated with oxygen, eq. (5.344) is used and the resulting conversion is 24.8%, higher as expected. b. Packed bubble bed reactor: The coefficient ko is 1 1 1 k 5 103 s1 k ( k f a u ) A b s k m Then, for zero inlet liquid-phase concentration of oxygen, we use eq. (5.368), where m 0.199 and n 9.02 (eqs. (5.364) and 5.365) which results in the conversion of 23.6%. For saturated liquid with oxygen, eq. (5.344) is used and the resulting conversion is 24.9%, higher as expected. Note that in the packed bubble bed reactor, the conversion achieved is higher by 14.02% due to the higher mass transfer coefficients compared to the trickle bed, especially the gas–liquid one, which is 3.61 times higher in the packed bubble bed reactor. In the case of saturated liquid feed, the conversion achieved is almost identical for both reactors. This is why in the respective reactor model, the gas-phase mass transfer is theoretically infinite and the difference in the liquid–solid mass transfer between the reactors is small, only 1.21 times higher than that in the packed bubble bed reactor.


481

5.3.6 Fluidized beds In this section, some analytical solutions of fluidized-bed models are presented. Specifically, model solutions will be given for the case of a gas-phase reactant and a single solid-catalyzed reaction of the form A → products and bubbling fluidized bed (Type B fluidization). The same analysis holds for a reaction of the form A B → products, if the reaction depends only on the concentration of A. Some solutions for the cases of a single reversible reaction, for two reactions in parallel, and two reactions in series will be given as well. The solutions of the model for particulate fluidization are the same as in two-phase fixed beds by employing the fluidized bed porosity instead the fixed-bed one. An example for this case will be also given. Two-phase models Emulsion phase in completely mixed state General solution—bubble phase free of solids (Orcutt model): In the following, the simplified Orcutt–Davidson–Pigford model (Orcutt et al., 1962) is presented. This model assumes in addition that the bubble phase is free of solids, and thus b 0. This means that the reaction takes place only in the emulsion (dense) phase. Then, the third term in eq. (3.516) disappears. The gas flow is in the inlet of the bed usA, the particulate phase ufmA, and the bubble phase usufmA, where A is the cross-sectional area of the bed and (eq. 3.476) bub

us ufm us ufm bub ubub ubub

(5.395)

Integrating eq. (3.516) with the boundary condition Cb Ci at z 0,  L z Cb Cp (Ci Cp ) exp  be   ubub 

(5.396)

Substitution of eq. (5.396) into the material balance for the particulate phase (eq. (3.518)) gives an equation for Cp, which can be solved analytically for certain reaction kinetics. Finally, the reactant concentration at the exit of the bed Co is Co

ufm Cp (us ufm ) Cb,o us

(5.397)

where the reactant concentration in the bubble phase at the bed exit Cb,o can be evaluated using eq. (5.396) for z Zf:  L Z  Cb,o Cp (Ci Cp )exp  be f   ubub 

(5.398)

482

5. Catalysis

Using the eq. (5.397), Cp from the material balance in the particulate phase eq. ( 3.518) and Cb,o from eq. (5.398), the exit concentration of the reactant can be evaluated. Analytical solutions: For the case of a first-order irreversible reaction (n 1) in the particulate phase (A;B), (rp,vs ) kvsCp

(5.399)

The solution of the model is Co (1 fbub e )2 (1 fbub e ) Ci kR (1 fbub e )

(5.400)

kvs Z fm (1 fm ) n1 kvs Z fm (1 fm ) Co us us

(5.401)

x 1

where kR

Lbe Z f L Z L Z be f be f ubub bub fbub us us ufm

(5.402)

where Lbe is the mass transfer coefficient between particulate and bubble phase in (m3 gas interchange gas volume/m3 reactor)(1/s) and kvs is the rate coefficient based on the volume of solids in m3 liquid/s m3 solids. The parameter is related to the bubble size and generally decreases as bubble size increases, and so small bubbles are preferred. Although the Orcutt–Davidson–Pigford model is simple, it does allow us to explore the effects of operating conditions, reaction rate, and interface mass transfer on the performance of the fluidized bed reactor. kvsZfm(1p) C In Figure 5.18, the conversion is, 1 o while kR . The value of C u

冢

i

冣

s

fbub is 0.5, i.e. the operating superficial velocity is only two times the minimum fluidization velocity. The parameter kR expresses the rate of reaction, i.e a measure of how fast the reaction is. At the same time, is a measure of the mass transfer rate. It is obvious that for a slow reaction (kR 1), the overall conversion of the fluidized bed is insensitive to mass transfer, and thus to bed hydrodynamics , while for intermediate and fast reactions (kR 1) the conversion is very sensitive to mass transfer expressed by and the flow distribution parameter fbub, and thus the conversion is determined by the bed hydrodynamics (Grace, 1984). This happens because in the absence of particles in the bubble phase, the gas reactants must reach the emulsion phase before the reaction can take place. The effect of mass transfer and flow distribution becomes more important for reactions of higher order (Grave, 1984). Finally, as expected, a high mass transfer rate, i.e. high , results in higher conversions. These results are typical for a gas-phase catalytic reaction in a fluidized bed.


483

1 0.9 0.8

conversion

0.7

χ=10 χ=2 χ=0.5 χ=0.1 χ=0

0.6 0.5 0.4 0.3 0.2 0.1 0 0.01

0.1

1 kR

10

100

Figure 5.18 Conversion as a function of reaction rate and interface mass transfer kR for fbub = 0.5 for a first-order gas-phase catalytic reaction.

For the case of a zero-order irreversible reaction (n 0) in the particulate phase (A→B), the solution of the model is (Grace, 1984) x 1

Co kR Ci

(5.403)

where kR

kvs Z fm (1 fm ) usCi

(5.404)

For the case of a half-order irreversible reaction (n 1/2) in the particulate phase (A;B), the solution of the model is (Grace, 1984)

x 1

0.5   Co 4(1 fbub e )2   kR2  1 1   Ci 2(1 fbub e )2   kR2   

(5.405)

where kR

kvs Z fm (1 fm ) usCi0.5

(5.406)

484

5. Catalysis

For the case of a first-order reversible reaction (n 1) in the particulate phase (A ↔ B), the solution of the model is (Grace, 1984) x 1

Co (1 fbub e ) (1 fbub e )2 Ci

1

2   kR1 (1 f e ) k  bub R1  kR1 K e (1 fbub e )  

(5.407)

where kvs,1 is for the forward and kvs,2 is for the reverse reaction kR1

kvs,1 Z fm (1 fm ) us Ke

(5.408)

kvs,1 kvs,2

(5.409)

For the case of a first-order consecutive reaction (n 1) in the particulate phase kvs,1 kvs,2 (A P Q) the solution of the model is (Grace, 1984)

;;

x 1

CA,o CA,i

yP

(1 fbub e )

(1 fbub e )2 kR (1 fbub e )

(1 fbub e )2 kR1 1 kR2 fbub e 1 fbub e kR1

(5.410)

(5.411)

where kR1

kR2

kvs,1 Z fm (1 fm ) us

(5.412)

kvs,2 Z fm (1 fm ) us

(5.413)

The yield of a product P is defined as the amount of product divided by the amount of A fed (initial amount): yP

CP CA,i

(5.414)


485

For the case of a second-order irreversible reaction (n 2) in the particulate phase (A → B), the solution of the model is (Grace, 1984) Co (1 fbub e ) Ci 0.5   (1 fbub e )2  4 kR  1  1 2 kR  1 fbub e    

x 1

(5.415)

where kR

kvs Z fm (1 fm ) Co us

(5.416)

The Orcutt model is very simple, offering analytical solutions, and thus is a useful tool for a rough estimation of the effect of various parameters on the operation of fluidized beds (Grace, 1984). However, it should be used only for qualitative comparisons, since its predictions have often been inaccurate compared to the experimental values obtained. The sources of those failures are the predicted uniform concentration of gas in the dense phase, which is not the case in experiments, and the assumption of the absence of solids in the bubble phase, which results in underestimating the conversion in the case of fast reactions. Emulsion phase gas in plug flow Solutions for bubble phase free of solids: In the following, a simplified solution is presented under the following assumptions: first-order reactions, gas flow only through the bubble phase (fb 1), and absence of solids in the bubble phase (b 0). Under these conditions, the material balances (3.519) and (3.520) become the following. For the bubble phase (gas phase),

us

d Cb Lbe (Cb Cp ) 0 dz

(5.417)

For the particulate phase (dense phase), Lbe (Cb Cp ) s kvsCp

(5.418)

where s is the solids holdup in the reactor (solids volume per volume of reactor) (eq. 3.526): s (1 bub ) (1 mf )

(5.419)

486

5. Catalysis

Note that the reaction rate is in (moles/m3 solid) (1/s), and thus by multiplying with s m3 solid/m3 reactor) we obtain the material balance in (moles/m3 reactor) (1/s). Then, solving eq. (5.418) for Cp:

Cp

Lbe Cb s kvs Lbe

(5.420)

Substituting in eq. (5.417) and after integrating in terms of conversion,

x 1

 Z  Co 1 exp  f  Ci  us 

(5.421)

where

Lbe s kvs s kvs Lbe

(5.422)

Kelkar and Ng (2002) present some other cases of first-order reactions. They define the following modulus: Nm

Nr

Lbe Z f us

kvs s Z f us

(5.423) (5.424)

Using this modulus, the previous solution becomes  N N  x 1 exp  m r   Nm Nr  Parallel reactions of first order k

vs,1 A  →P k

vs,2 A  →Q

(5.425)


487

The fractional conversion of A is  N ( N N r2 )  x 1 exp  m r1   N m N r1 N r2 

(5.426)

 N r1  yP  x  N r1 N r2 

(5.427)

 N r1  sP   N r1 N r2 

(5.428)

where the selectivity to P is defined as the amount of product divided by the amount of A reacted:

sP

CP CA,o CA,out

(5.429)

Reactions of first order in series k

k

vs,1 vs,2 A  → P  →Q

The fractional conversion of A is  N N  x 1 exp  m r1   N m N r1 

(5.430)

 N r1     N m N r2  N N  yP  exp  m r1   exp     N r1 N r2    N m N r2   N m N r1  

(5.431)

  N m2 ( N r1 N r2 ) exp   1 ( )( ) N N N N  N r1  r1 m r2   m sP   N r1 N r2   N N  exp  m r1  1  N m N r1 

(5.432)

Solutions for solids presence in bubble phase: This model has been proposed by Chavarie and Grace as a two-phase simplification of the three-phase Kunii–Levenspiel model (Grace, 1984). Here, the solution is given under the following assumptions: firstorder reaction, gas flow only through the bubble phase (fb 1) and absence of solids in

488

5. Catalysis

the bubble phase (b ≠ 0). Under these conditions, the material balances (3.519) and (3.520) become the following. For the bubble phase (gas phase), us

d Cb Lbe (Cb Cp ) bub b (rb,vs ) 0 dz

(5.433)

For the particulate phase (dense phase), Lbe (Cb Cp ) (1 bub ) (1 mf )(rp,vs ) 0

(5.434)

For the case of a first-order irreversible reaction (n 1) in the particulate phase (A → B), the solution of the model is x 1

k 冤 ( ) kG b bub s 冥  Co 1 exp  G G b bub s  G kG s Ci   s (1 bub )(1 mf )

(5.435)

(5.436)

where kG

kvs Z fm us

(5.437)

G

Lbe Z f us

(5.438)

This model is simpler that the Kunii–Levenspiel model and eliminates the unsubstantiated expression for cloud-to-emulsion transfer employed by Kunii and Levenspiel (Grace, 1984). Furthermore, compared to the previous models, the introduction of the parameter b in the model leads to better results as the assumption that there is no solids in the bubble phase may lead to the underestimation of conversion in fast reactions. For slow reactions, the value of b is of minor importance. However, for fast reactions the model may become sensitive to this parameter and the actual conversion should be bounded between the predicted ones using the upper and lower limits of b, i.e. 0.01 and 0.001, respectively (Grace, 1984). Kunii–Levenspiel three-phase model (bubbling bed model) Here, the simple case of a first-order reaction is presented. The intrinsic reaction rate in each phase is rvs kvsCA

(5.439)


489

Using the material balances for the cloud and the emulsion phase, eqs. (3.537) and (3.538), it is possible to eliminate the concentrations Cc and Ce. Then, from eq. (3.536), 1  (rb,u )overall  b kvs  Cb

 

(5.440)

The terms in the brackets represent the complex series–parallel resistances to mass transfer and reaction. The parameter is

1 K bc

1 c kvs

1 1K ce 1 (e kvs )

(5.441)

For very fast reaction and thus large kvs,

⬵

1 K bc

(5.442)

Under this condition, the reactant A is unlikely to reach the emulsion phase. Integrating the material balance for the bubble phase, eq. (3.534) yields the desired performance expression in terms of conversion:

x 1

Co 1 Ci

1  1 Z  exp  b kvs  f  

 ubub  

(5.443)

Levenspiel proposed that approximately,  1  Z Zf ⬵ ubub  1 fm  ubs

(5.444)

where Z and is the fixed bed height and porosity, respectively. In Figure 5.19, the conversion versus T Zf /ubub is presented. The following typical values are used: b 0.005, c 0.2, e 1.5, Kbc 2 s1 and Kce 1 s1. It is obvious that to have high conversion, the reaction should be fast enough and the term Zf /ubub should be high. Hence, ubub should be small or, in other words, dbub should be small. The effect of mass transfer resistance is shown in Figures 5.20 and 5.21 for the cases of a fast (kvs 1 s1) and a slow reaction (kvs 0.01 s1), respectively.

490

5. Catalysis

1

conversion

0.8

0.6

0.4 k = 0,01 0.2

k = 0,1 k=1

0 1

10

100

T (s)

Figure 5.19

Conversion versus T Zf /ubub for kvs = 0.01, 0.1 and 1 s1.

Kbc=0,2, Kce=0,1

1

Kbc=2, Kce=1 Kbc=20, Kce=10

conversion

0.8

0.6

0.4

0.2

0 1

Figure 5.20

10 T (s)

100

The effect of mass transfer resistance for a slow reaction (kvs = 0.01 s1).

As in the Orcutt model, for a slow reaction, i.e. a small kvs, the overall conversion of the fluidized bed is less sensitive to mass transfer and thus to bed hydrodynamics than for a fast reaction, i.e. large kvs. Comments on the models The basic differences between models are the following: • • •

the flow distribution status, expressed by fbub, the solids presence in the bubble phase, expressed by b, and the mixing state in the emulsion phase.


491

1

conversion

0.8

0.6 0.4 Kbc=0,2, Kce=0,1 Kbc=2, Kce=1 Kbc=20, Kce=10

0.2 0 1

10

100

T (s)

Figure 5.21

The effect of mass transfer resistance for a fast reaction (kvs = 1 s1).

These features are summarized in Table 5.20. It has been stated that the effect of fbub and b is greatly dependent on the parameter kR, and thus basically on the reaction coefficient. For a slow reaction, kR is much less than unity and the effect of fbub and b is minimal, whereas the opposite holds for a fast reaction, where kR is greater than unity. Furthermore, the Orcutt–Davidson–Pigford model has proved on some occasions to deviate from experimental results, probably due to the choice of the value of fbub (Grace, 1984). At the same time, it is the only model that takes into account the effect of this parameter. Taking into account all these observations, some guidelines for the use of these models are presented in Table 5.21. Finally, in general, the models are less sensitive to the assumed gas-mixing status in the emulsion phase, i.e. complete mixing or plug flow, than to the expressions used to determine the interphase mass transfer coefficient (Grace, 1984). Example 17 Masimilla and Johnstone studied the catalytic oxidation of ammonia: 4NH3 (g) 3O2 (g) 2N 2 (g) 6H 2 O (g) at 1.11 atm and 250 °C, in a fluidized-bed reactor of D 11.4 cm (Masimilla and Johnstone, 1961; Werther, 1980). The reaction is of first order with respect to ammonia and the rate coefficient is kvs 0.00858 (m3/m3) (1/s). The catalyst is spherical with a particle density of 2060 kg/m3 and minimum fluidization velocity of 0.014 m/s. The bed height at incipient fluidization is Zfm 58 cm.

492

5. Catalysis

Table 5.20 The main features of the models

fbub b Mixing state

Orcutt–Davidson– Pigford

Kelkar–Ng

Chavarie– Grace

Kunii– Levenspiel

Variable 0 Complete mixing

1 0 Plug flow

1 Variable Plug flow

1 Variable Plug flow

Table 5.21 Guidelines for the use of fluidized-bed models Flow distribution parameter

Reaction parameter

Proposed model

fbub 1 fbub 1

kR 1 kR 1

fbub 1

kR 1

fbub 1

kR 1

Orcutt–Davidson–Pigford Orcutt–Davidson–Pigford Kelkar–Ng Chavarie–Grace Kelkar–Ng Chavarie–Grace Kunii–Levenspiel Chavarie–Grace Kunii–Levenspiel

The reaction gas (10% NH3 and 90% O2) flows through a porous stainless steel distributor under a superficial velocity in the range 0.025–0.15 m/s. The resulted conversion under the specified conditions is shown in Table 5.22 (approximate values). Use all the simplified models to estimate the conversion and comment on the results. Assume that 0.625, b 0.0055 (average values), and zero expansion factor. Solution For all calculations, we need the characteristic properties of the gas phase. Since the gas phase consists of 90% oxygen, we can assume that the gas properties can be represented by the properties of the oxygen alone. The gas density can be evaluated using the ideal gas law MB

P 0.828 kgm3 RT

The correlation of Jossi et al. (see eq. (I.12) in Appendix I) cannot be used for the determination of gas viscosity, as it is valid only for high pressures. Instead, Table I.2 in Appendix I can be used, where we find that the viscosity at 523 K is 3.16 105 kg/m s. The next parameter required for the gas phase is the diffusion coefficient of the species that


493

Table 5.22 Conversion versus us x (%)

us (m/s)

61 38 25 16

0.025 0.05 0.1 0.15

control the reaction rate. The Fuller–Schettler–Giddings equation (see eq. (I.29) in Appendix I) can be used for the determination of the diffusion coefficient of nonpolar gases in binary mixtures at low pressures:

DBA 0.01

T 1.75 M R0.5 6.16 105 m 2 s 1 3 2 1 3 P 冢VA VB 冹

where T is the temperature in K, P the pressure in Pa, and VA and VB the diffusion volumes of gases. The diffusion volume of ammonia is 14.9 cm3/mol and that of oxygen is 16.6 cm3/mol (see Table I.12 in Appendix I). Finally, MR is (see eq. (I.30) in Appendix I) MR

1 1 0.09 MBA MBB

where MBA and MBB are the molecular weights of the gases. The following calculations are based on the superficial gas velocity of 0.1 m/s and can be repeated equally for different gas velocities. Hydrodynamics: The first and most important analysis in fluidized beds is the hydraulic one. As we see from the data given, the particle size is missing. However, the superficial velocity at incipient fluidization ufm has been provided, and thus the particle size can be evaluated using the Ergun equation (3.451) by trial and error. At first, we have to evaluate the bed porosity at incipient fluidization for the assumed particle size using the relationship of Broadhurt and Becker (eq. 3.466). Note that the resulting value cannot be lower than the fixed-bed porosity. Since we assume spherical particles, a reasonable value of bed porosity is 0.41. This procedure results in fm 0.44 and a particle size of 0.145 mm. The fixed-bed length can be estimated from the given bed length at incipient fluidization (eq. 3.470): Z Z fm

1 fm 54.69 cm 1

494

5. Catalysis

The Mori–Wen correlation describes the initial bubble size for porous plates (eq. 3.502): dbub,o 0.00376 (us ufm )2 0.28 cm The maximum bubble size can be calculated as follows (eq. 3.504):   dbub,max 0.652  D 2 (us ufm )  4  

0.4

9.81 cm

In these equations, CGS units should be used. Then, the actual bubble diameter at height z above the bottom of the bed is (eq. 3.505) dbub,max dbub dbub,max dbub,o

 0.3z  exp   D 

In order to calculate an average value of the bubble velocity, an average bubble diameter should be used. This diameter can be taken to be equal to the bubble diameter at z Zf/2. Thus, to calculate the bubble diameter, the fluidized bed height should be known (eq. 3.477): Zf

Z fm 1 bub

bub

us ufm ubub

where (eq. 3.476)

The rise velocity of bubble, cloud and wake is (eq. 3.501) ubub us ufm 0.711( gdbub )0.5 The rise velocity of a single bubble in a fluidized bed is given by (eq. 3.500) ubs 0.711( gdbub )0.5 An iteration method has to be used to solve the problem (see Figure 3.60). The initial value of the fluidized bed height is the bed height at incipient fluidization. The results of the iteration process, for us 0.1 m/s, are shown in Table 5.23.


495

Table 5.23 Hydraulic parameters Parameter

Value

dbub (cm) ubs (cm/s) ubub (cm/s) bub f Zf (cm)

5.87 53.94 62.54 0.14 0.52 67.25

For the Kunii–Levenspiel model, we need some additional hydraulic parameters. The fraction of the bed occupied by bubbles is given by (eq. 3.494)

us ufm 0.143 ubub ufm (1 )

where is the volume of wake per volume of bubbles and its average value is 0.625. Then, the fraction of the bed occupied by clouds is (eq. 3.497)

c

3ufm 0.027 fm ubs ufm

Then from eqs. (3.498) and (3.499), we have   3ufm   c (1 fm )   (1 fm )  c  0.451    fm ubs ufm 

e

(1 fm )(1 ) (c b ) 2.89

where b is the volume of the solids dispersed in bubbles per volume of bubbles, c is the volume of the solids within clouds and wakes per volume of bubbles, and e is the volume of the solids in emulsion per volume of bubbles. The average value of 0.0055 is used for b. At this point, it is important to know if the operating gas superficial velocities, which are from 2.5–15 cm/s, are higher than the minimum bubbling velocity and lower than the minimum slugging velocity, respectively.

496

5. Catalysis

The minimum bubbling velocity can be calculated as (eq. 3.481) ubm

2.070.06 G dp exp (0.716 X 45 ) 0.347 G

1.08 cms

Since, the particle size used is greater than 45 µm, the value of (X45) is zero. The above equation holds for Group A particles. Using Figure 3.52, we see that for ≅ 2000 kg/m3, the Group A particles region is approximately between 0.03 and 0.16 mm. In our case, we have 0.145 mm and thus the particles belong to the Group A. However, this does not necessarily means that the fluidization is of Type A. This is why the reaction conditions are far from ambient. Furthermore, the resulting bubbling velocity is less than the minimum fluidization velocity (1.4 cm/s) indicating that the above equation is not adequate for our case. Here, we can use the Romero-Johnston criterion (eq. (3.488)) which is clearly indicates that the fluidization is bubbling. The minimum slugging velocity can be evaluated as (eq. 3.491) usm ufm 0.16 (1.34 D 0.175 Z fm )2 0.07( gD)0.5 10.61 cms Then, for velocities higher than 10.61 cm/s, it is expected that slugging will occur in the bed. In our case, the highest velocity is 15 cm/s, and for this velocity dbub 7.37 cm. However, the maximum bubble diameter is 11.78 cm, essentially equal to the bed diameter, which is 11.4 cm. Thus, the results of these high velocities could be suspicious, as the fluidization regime is no more bubbling but slugging, or is in the transition region. Mass transfer: For the Kunii–Levenspiel model, the mass transfer coefficient of the gas between bubble and cloud is (eq. 3.544) K bc 4.5

Dg1 2 g1 4 ufm 5.85 3.88 s1 5 4 dbub dbub

Here, CGS units are used. The mass transfer coefficient of the gas between cloud and emulsion is (eq. 3.545)  fm Dg ubub  K ce ⬵ 6.78   3   dbub

1 2

1.97 s1

On the other hand, for the two-phase models, we need the mass transfer coefficients per unit volume of reactor (eq. (3.547)): Lbe k be bub


497

where kbe is the mass transfer coefficient in m/s between bubble and emulsion (eq. (3.553)):  4 mf Dg ubub  u kbe fm  3  dbub 

1 2

2.39 102 ms

and abub is the interfacial area of the bubble phase per reactor volume (m2 bubbles/m3 reactor) (eq. (3.549)): abub

6 bub 14.05 mm 2 dbub

Then, Lbe 0.34 s–1. Models: The parameters of Orcutt model (eq. (5.400)) are the following (us 0.1 m/s) (eqs. 3.517, 5.401, and 5.402): us ufm 0.86 us

fbub

kR

kvs Z fm (1 fm ) 0.28 us

Lbe Z f 2.63 ubub bub

The only parameter we need for the Ng–Kelkar model (eq. (5.421)) is the following (eq. (5.422)):

Lbe s kvs 0.037 s kvs Lbe

where (eq. (3.526)) s (1 bub )(1 fm ) 0.48 For the Chavarie–Grace model (eq. 5.435) we need two parameters (eqs. (5.437) and (5.438)): kG

kvs Z fm 0.498 us

G

Lbe Z f 2.26 us

498

5. Catalysis

Finally, for the Kunnii–Levenspiel model (eq. (5.443)) we need one more parameter (eq. (5.441)):

1 K bc

1 c kvs

1 1K ce 1 (e kvs )

4.12

In order to have an overview, the basic characteristics of the four models used as well as the conversions achieved for us 0.1 m/s, are shown in Table 5.24. The only model that takes into consideration the fraction of the gas that flows through the emulsion phase is the Orcutt–Davidson–Pigford one and for the specific superficial velocity of 0.1 m/s, fbub 0.86. The results of the models are close due to the fact that the flow Table 5.24 Conversion for each model Model

x (%)

Orcutt–Davidson–Pigford Kelkar–Ng Chavarie–Grace Kunii–Levenspiel

21.4 22.9 19.5 23.0

90 Kunii-Levenspiel 80 Orcutt-DavidsonPigford

70

Kelkar-Ng

X (%)

60 Chavarie-Grace

50

Experimental

40 30 20 10 0 0

0.02

0.04

0.06

0.08

0.1

0.12

us (m/s)

Figure 5.22

Comparison of models.

0.14

0.16


499

distribution parameter is of minor importance as a result of a slow reaction (kR 0.28). Furthermore, these values are close to the experimental one (approximately 25%). All calculations can be repeated for the other superficial velocities and the results are shown in Figure 5.22. It is evident that the Kunii–Levenspiel model deviates a lot for low values of superficial velocity and thus low fbub, which shows that it cannot be used in this region. However, for a high superficial gas velocity, its predictions are slightly better than the other three models. Thus, with the exception of the Kunii–Levenspiel model at low values of fbub, these models have a good behavior for the specified system. A few comments on ammonia: Ammonia (NH3) is a gaseous compound at standard temperature and pressure with a characteristic odor, which is toxic and corrosive to some materials. It is used extensively in the production of fertilizers, explosives, and polymers. It is also produced naturally by the putrefaction of nitrogenous animal and vegetable matter. Ammonium salts are also ingredients of fertile soil and seawater. Being one of the most highly produced inorganic chemicals, ammonia is today mainly manufactured via the Haber process. Nitrogen and hydrogen react at 200 bar and 500 °C over an iron catalyst to produce ammonia: N 2 3H 2 2 NH 3 Ammonia is not hazardous to humans and other mammals because the existence of a specific mechanism in their bodies leads to the conversion and excretion of ammonia. However, fish and amphibians lack this mechanism, and as a result ammonia is very toxic and dangerous to the aquatic environment. Example 18 Zhang and Hu (2004) studied the catalytic oxidation of carbon monoxide: 1 CO (g) O2 (g) CO2 (g) 2 The catalyst used is commercial Type 111 Cu/Cr/Ag impregnated, coal-based carbon of 0.6 mm particle size and bulk density of 0.7 g/cm3. This study confirmed that for particle size lower than 0.6 mm the internal diffusion is negligible, and the reaction is first order in respect to CO concentration. The feed is air with CO concentration of 180 ppm, and thus the expansion factor is zero. Some of the experimental results at 50 °C and for dp 0.3 mm are shown in Table 5.25 (approximate values). Estimate the reaction coefficient km at 50 °C. Then, assume that we run the reaction in a fixed bed of 1.61 m diameter and 16.1 m height, under a contact time of 5 min. Estimate the conversion of CO in the fixed-bed reactor, and under the same conditions, in a fluidized-bed reactor, with the same initial (fixed bed) height. For P 1 atm and T 50 °C the air properties are: 1.09 kg/m3 and 2 105 N s/m2 and the diffusion coefficient of CO is 0.160 cm2/s. Assume that the fixed-bed porosity is 0.41 and that the particles are spherical.

500

5. Catalysis

Table 5.25 Experimental results

(min)

x

5 10 15 20

0.38 0.60 0.75 0.80

Solution Fixed bed: The performance equation in a fixed-bed reactor is (eq. (5.192))  1  k

Z ln   1 xA  us,i Then, the coefficient ko is the slope of the line  1  y ln  k  1 xA  This plot is shown in Figure 5.23. The slope of the best-fit line is 0.0014 s1 while R2 0.9646. Then (eq. 5.193) 1 1 1 k ( k f au ) A b k m

2

ln [1/(1-X)]

1.6 1.2 0.8 0.4 0 0

200

400

Figure 5.23

600 800 τ (s)

1000

The estimation of ko.

1200

1400


501

To evaluate the rate coefficient km, we need the external mass transfer coefficient. Using the following correlation (eq. (3.352)):  Rep  Sh   

0.5

Sc 0.33 1.53

and thus kf 8.16 × 102 m/s. Furthermore, au 11,800 m2/m3 and thus km 2 × 106 m3/kg s. For the fluidized-bed models, the coefficient kvs is needed: kvs p km

b km 2.37 103 m 3 m 3 s (1 )

For the specified bed length and 5 min contact time, the superficial gas velocity is 5.37 cm/s. Fluidized bed: For the specified system and particle size, the minimum fluidization velocity is 4.24 cm/s (eq. 3.451). Here, we note that the operating superficial velocity in the fixed bed is higher than the minimum fluidization velocity. This means that to retain a fixed-bed operation, we should operate in a downflow mode. The (superficial) residence time in the fluidized bed can be defined as in a fixed-bed operation (eq. (3.98)):

f

VR Z f Q us

Then, taking into account that the fluidized bed height is higher than the fixed-bed height and to have the same residence time of 5 min, the superficial velocity should be higher as well. Here, an iteration procedure is needed to evaluate the Zfus couple sufficient to give the desired contact time of 5 min. The iteration can start by choosing a low superficial velocity, near but higher than the minimum bubbling velocity. The results of the iteration procedure are shown in Table 5.26. Then, we can use OrcuttDavidsonPigford model (eq. 5.400), where the following parameters can be evaluated from the above data: fbub 0.23, kR 0.41, 5.50, and Lbe 4.15 × 103 s1. Then, the conversion is equal to 29.1%, lower than the conversion evaluated by the corresponding model for fixed-bed operation, which is 34.3%. By increasing the superficial velocity, the fluidized bed height is increased. However, it is easy to show that for a given particle size the contact time is lower for higher velocities, and that the highest contact time can be achieved by working under the lower allowable superficial velocity, which should be higher than the minimum fluidization velocity. This is shown in Figures 5.24 and 5.25.

502

5. Catalysis


Value

us (cm/s) ufm (cm/s) dbub (cm) ubs (cm/s) ubub (cm/s) bub f Zf (m)

5.48 4.24 29.46 120.81 122.05 0.01 0.42 16.44

350 300 250

τ (s)

200 150 100 50 0 4

5

Figure 5.24

6

7 8 us (cm/s)

9

10

11

Residence time versus superficial velocity.

If we use smaller particle size, the fluidised bed performance is different. For example, for dp = 0.15 mm, the minimum fluidization velocity is 1.34 cm/s. If we operate the fluidized bed with this particle size, to have a contact time of 5 min, we need a superficial velocity of 5.85 cm/s, where the fluidized bed height is 17.51 m, and the resulting conversion is somewhat lower, equal to 25.8 %. In this example, we show that for the same superficial contact time, the fluidized bed results in a different conversion, which is not the case for a fixed-bed reactor. As will be presented in Chapter 6, the complicated hydrodynamics of fluidized beds does not allow such simple “scale-up” rules to emerge.


503

16.9

Z f (m)

16.8

16.7

16.6

16.5

16.4 4

Figure 5.25

5

6

7 8 us (cm/s)

9

10

11

Fluidized bed height versus superficial velocity.

A few comments on carbon monoxide: Carbon monoxide is a deleterious gas that is mainly produced by the incomplete combustion of carbon-containing fuels. The exposure to carbon monoxide results in various undesirable health effects. Carbon monoxide is adsorbed through the lungs and reacts with hemoglobin to form carboxyhemoglobin, which impairs the capability of blood to carry oxygen. In addition to this mechanism, carbon monoxide combines with myoglobin, cytochromes, and metalloenzymes. However, the binding of carbon monoxide to hemoglobin appears to be the principal action mechanism underlying the toxic effects of exposure to low levels of carbon monoxide. High levels of exposure to carbon monoxide can cause death. Carbon monoxide is a pollutant frequently met in the urban environment, since it is produced via any combustion of fossil fuels, with motor vehicles being the most important source. Fortunately, the action of carbon monoxide on human health is reversible after withdrawal from exposure. Example 19 Shen and Johnstone studied the catalytic decomposition of nitrous oxide: 2N 2 O (g) 2N 2 (g) O2 (g) at 1 atm and 371 °C in a fluidized-bed reactor of D 11.4 cm (Shen and Johnstone, 1955; Werther, 1980). The reaction is of first order in respect to nitrous oxide. The catalyst is spherical and its data are: p 2060 kg/m3 and ufm 0.005 m/s. The bed height at incipient fluidization Zfm 109 cm.

504

5. Catalysis

The reaction gas (1% N2O and 99% air) flows through a porous stainless steel distributor under a superficial velocity of 0.01 m/s. The resulting conversion under the specified conditions is approximately 25%. Evaluate the rate coefficient of the reaction using the OrcuttDavisdonPigford model. Assume that the internal effectiveness factor is unity and the expansion factor zero. Solution For all calculations, we need the characteristic properties of the gas phase. The air density can be evaluated by using the ideal gas law:

MB

P 0.548 kgm 3 RT

The air viscosity can be found in Table I.2, Appendix I, where we find that the viscosity at 644 K is about 3.1 × 105 kg/m s. The Fuller—SchettlerGiddings equation is proposed for the determination of the diffusion coefficient of nonpolar gases in binary mixtures at low pressure (see eq. (I.29) in Appendix I):

DBA 0.01

T 1.75 M R0.5 5.44 105 m 3 s P 冢VA1 3 VB1 3 冹2

where T is the temperature in K, P the pressure in Pa, and VA and VB the diffusion volumes of gases. The diffusion volumes of nitrous oxide and air are 35.9 cm3/mol and 20.1 cm3/mol, respectively (see Table I.12 in Appendix I). Finally, MR is (see eq. (I.30) in Appendix I) MR

1 1 0.057 MBA MBB

where MBA and MBB are the molecular weights of the gases. Hydrodynamics: The particle size can be evaluated from the superficial velocity at incipient fluidization ufm and the Ergun equation by trial and error (eq. (3.451)). For this calculation, we need the bed porosity at incipient fluidization for the assumed particle size, which can be evaluated by using the relationship of Broadhurt and Becker (eq. (3.466)). Note, that the resulting value cannot be lower than the fixed-bed porosity. Since we assume spherical particles, a reasonable value of bed porosity is 0.41. This procedure results in a particle size of 0.077 mm and fm 0.47. The fixed-bed length can be evaluated from the given bed length at incipient fluidization (eq. (3.470)): Z Z fm

1 fm 97.95 cm 1


505

The MoriWen correlation is used for the initial bubble size for porous plates (eq. (3.502)): dbub,o 0.00376(us ufm )2 9.4 104 cm The maximum bubble size is (eq. 3.504)   dbub,max 0.652  D 2 (us ufm )   4

0.4

3.14 cm

CGS units should be used in these equations. Then, the actual bubble diameter at height z above the bottom of the bed is (eq. 3.505) dbub,max dbub dbub,max dbub,o

 0.3z  exp   D 

To calculate an average value of the bubble velocity, an average bubble diameter should be used. This diameter can be taken to be equal to the bubble diameter at z Zf /2. Thus, to calculate the bubble diameter, the fluidized-bed height should be known (eq. (3.477)): Zf

Z fm 1 bub

bub

us ufm ubub

where (eq. 3.476)

The rise velocity of bubble, cloud, and wake is (eq. 3.501) ubub us ufm 0.711( gdbub )0.5 The rise velocity of a single bubble in a fluidized bed is given by (eq. 3.500) ubs 0.711( gdbub )0.5 To solve the problem, an iteration method should be used (see Figure 3.60). The initial value of the fluidized-bed height is the bed height at incipient fluidization. The results of the iteration process are shown in Table 5.27.

506

5. Catalysis


Value

dbub (cm) ubs (cm/s) ubub (cm/s) bub f Zf (cm)

2.41 34.55 35.05 0.01 0.48 110.58

At this point it is important to know if the operating gas superficial velocity, which is 1 cm/s, is higher than the minimum bubbling velocity and lower than the minimum slugging velocity. The minimum bubbling velocity can be calculated as follows (eq. 3.481):

ubm

2.070.06 G dp exp (0.716 X 45 ) 0.347 G

0.56 cms

Since, the particle size used is greater than 45 m, the value of (X45) is zero. The resulting minimum bubbling velocity is less than the operating velocity (1 cm/s). Thus, the operating regime is purely bubbling fluidization. The above equation holds for Group A particles. By using Figure 3.52, we see that for ≅ 2000 kg/m3, the region of Group A particles is approximately between 0.03 and 0.16 mm. In our case, we have 0.077 mm and thus the particles belong to the Group A. This explains why the minimum fluidization velocity, which is 0.5 cm/s, is lower than the minimum bubbling velocity, which is 0.56 cm/s. This type of particle exhibits two types of fluidization, i.e. particulate fluidization for velocities lower than ubm and bubbling fluidization for velocities higher than ubm. The minimum slugging velocity can be evaluated as follows (eq. 3.491):

usm ufm 0.16(1.34 D 0.175 Z fm )2 0.07( gD)0.5 8.38 cms Then, for velocities higher than 8.38 cm/s, which is much higher that the operating velocity, it is expected that slugging will occur in the bed. Mass transfer: For the two-phase models, we need the mass transfer coefficients per unit volume of reactor (eq. 3.547): Lbe kbe bub


507

where kbe is the mass transfer coefficient in m/s between bubble and emulsion (eq. 3.553):

kbe

ufm  4 mf Dg ubub  3  dbub 

1 2

2.34 102 ms

and abub is the interfacial area of the bubble phase per reactor volume (m2 bubbles/m3 reactor) (eq. 3.549): abub

6 bub 3.55 m 2 m 3 dbub

Then, Lbe 0.083 s1. Model application: The parameters of the OrcuttDavidsonPigford model (eq. 5.400) are the following: fbub

kR

us ufm 0.50 us

kvs Z fm (1 fm ) 57.79kvs us

Lbe Z f 18.41 ubub bub

For a conversion level of 0.25 and by trial and error, the resulting rate coefficient is 0.0058 m3/m3 s. Werther (1980) reported a value of 0.0051 m3/m3 s, under the same conditions.

A few comments on nitrous oxide: Nitrous oxide (N2O) is a gas produced mainly through natural sources. However, generally, motor vehicles and combustion processes contribute to its formation in urban areas. Unfortunately, it is involved adversely in the two global environmental problems: it contributes to the greenhouse effect and penetrates into the stratosphere, destroying the ozone layer.

Example 20 Menoud et al. (1998) studied the adsorption of copper from aqueous solutions by using the chelating resin Chelamine. The resin used is spherical, with an average diameter of 0.305 mm, swollen particle density of 158.4 kg/m3 (dry) and hydraulic particle density (wet density) of 1064 kg/m3.

508

5. Catalysis

The equilibrium of the copperresin system was studied in a batch reactor at 20 °C and maximum copper concentration of 1.3 mol/m3. The isotherm follows the Langmuir model with K 142.86 m3/mol and QM 0.97 mol/kg. The resin is packed in a bed of 5.2 cm diameter and 14.9 cm initial height while its porosity is 0.33. The fluidized bed is operated with 1.25 × 106 m3/s flow rate and initial copper concentration of 0.308 mol/m3 at 20 °C. Calculate the time needed for the exit concentration to reach 20% of the inlet one. The experimental value is approximately 20.18 h. Solution Equilibrium analysis: The Langmuir isotherm equation is (eq. (4.5)) 142.86Ce qe KCe q e QM 1 KCe 0.97 1142.86Ce Then, the equilibrium parameter La is (eq. (4.8)) La

1 1 0.0054 1 KCo 1142.86 1.3

This value indicates that the ion-exchange process is essentially irreversible, i.e. very favorable for the uptake of copper from solution. Hydraulic analysis: The porosity of the bed at incipient fluidization is evaluated by using the Limal–Ballesteros correlation (eq. 3.467): fm

0.42 0.42 S0.376

Then, the fluidized bed height at incipient fluidization is (eq. 3.470) Z fm Z

1 0.17 m 1 fm

The next step is to use the Ergun equation for the determination of the minimum fluidization velocity (eq. 3.451). For this step, we need a goal-seek procedure and the minimum fluidization velocity is found to be equal to 0.0057 cm/s, which is almost 10 times lower than the operation superficial velocity: us

Q 1.25 106 5.87 104 ms A 2.12 103

or 0.0587 cm/s. Note that for the determination of minimum fluidization velocity, the hydraulic density is used, i.e. the value of 1064 kg/m3. This density is used for all hydraulic calculations.


509

For the specified operating superficial velocity, the Reynolds number is equal to 0.2 while the Archimedes number is (eq. 3.454)

Ar

dp3 f ( h f )g 2f

22.59

The fluidized-bed porosity is calculated by using Pavlov’s correlation (3.475):  18 Rep 0.36 Rep2  f   Ar  

0.21

0.68

Finally, the fluidized-bed height at operation is (eq. 3.469) Zf Z

1 0.31 m 1 f

Mass transfer in the fluidized bed: Assuming that the diffusion coefficient of copper in the aqueous solution is approximately 109 m2/s, we have (eq. 3.359) Sc

( ) vf f f 902 Df Df

As long as the fluidization regime is particulate, we can use the correlation of Koloini–Sorpic–Zuner (eq. 3.543): Sh

0.7 Rep Sc1 3 1.98 f

Then (eq. 3.357) Sh

kf dp Df

kf 6.48 106 ms

The diffusion in the resin: The diffusion coefficient in the resin could be estimated by using the following relationship (eq. 3.607):  p  Ds Df    2 p 

2

510

5. Catalysis

The resin internal porosity can be evaluated by using the definition of the hydraulic density (eq. 3.558): h p f p p 0.91 Note that in this relationship, we use the swollen particle density, i.e. 158.4 kg/m3, because the resin volume changes when immersed in the solution. Then, the solid-phase diffusion coefficient is 6.9 × 1010 m2/s. The controlling mechanism: For the estimation of the controlling mechanism, the Miura and Hashimoto criterion is used (mechanical parameter) (eq. (4.207)):

k f au K (qmax Co )

K

15Ds b,f rp2

where (eq. (4.209))

au

6(1 f ) 6308.5 dp

In analogy to the fixed-bed bulk porosity (eq. 3.557), the fluidized-bed bulk density (b,f) is b,f p (1 f ) 158.4(1 0.91) 50.76 kgm 3 Note that the swollen particle density, which is 158.4 kg/m3, is used for all calculations except the hydraulic ones, where the hydraulic density is used. Then, we have K 22.61 and 0.0006. According to the mechanical parameter criterion, if is zero (practically much lower than 1), then fluid-film diffusion controls the process rate, while if infinite (practically much higher than 1), then solid diffusion is controlling the process rate. It is obvious that the controlling mechanism is the fluid-film diffusion. Analysis of the model: For the application of the model, we need the operating equilibrium loading of the resin qmax, which is different (lower) that the maximum loading QM evaluated in batch experiments. The equilibrium loading is a function of the maximum inlet concentration of copper, which is 0.308 mol/m3. By using the equilibrium relationship (eq. (4.5)), 142.86Co qmax qmax 0.95 molkg 0.97 1142.86Co

5.4 Catalyst Deactivation

511

The appropriate model for the case of film-diffusion control is (eq. (4.140)) N f (T 1)

ln(0.2) 0.0054 ln(1 0.2) ln( X ) La ln(1 X ) 1 1 0.62 1 0.0054 1 La

Then N f (T 1) 0.62 T 0.97 Furthermore (eq. (4.135)),

b,f qmax Co

156.57

The volume of the fluidized bed is Vf Z f A 6.61104 m 3 Then (eq. (4.137)) Nf

kf auVf 21.69 Q

Finally (eq. (4.136)), V  t  f f   Q  t 22.51h T  Vf   Q  The difference from the experimental value (20.18 h) is 11.5%. Taking into account the complexity of the system, the deviation could be considered satisfactory.

5.4 CATALYST DEACTIVATION A major problem associated with the operation of heterogeneous catalysts is their gradual loss of activity with time. Inevitably, chemical and/or physical parameters affect the action of catalysts and progressively lead to its partial or total catalyst deactivation. Deactivation processes occur simultaneously with the main reactions.

512

5. Catalysis

Deactivation may proceed through chemical or physical processes, which are generally classified into • • •

poisoning fouling and coking thermal degradation.

In the following, the general aspects of these phenomena are presented. 5.4.1 Poisoning Poisoning is characterized as the loss of catalyst activity due to the chemisorption of reactants, products, or contaminants in the feed onto catalyst active sites, thus occupying sites otherwise available for catalysis. In environmental catalysis, the presence of impurities hostile to the catalyst is the common case of deactivation. For example, components of lubricants may accumulate on the surface of a three-way catalyst and progressively cause its deactivation. Whether this process is irreversible or not depends on the nature of the poisoning agent. Here, we should make a distinction between poisons and inhibitors. An inhibitor is a substance weakly adsorbed, and generally its removal from the feed is sufficient for the catalyst to regain its activity, or at least a part of it. So, this type of poisoning is reversible and could be referred to as “inhibition.” On the other hand, species that interact strongly and irreversibly with the active sites are characterized as poisons and lead to chemical irreversible deactivation. The distinction is not always clear, since a poison that is very harmful at low temperatures may have an unimportant effect on catalyst activity at high temperatures. A poison may simply block the active site, or affect the adsorptivity of other substances. Moreover, it may change the chemical nature of the active sites or form complexes with them, thereby definitely modifying their action. Moreover, the action of poisons can be nonselective or selective. In the first case, the poisoning agent affects all the active sites in the same manner. So, the activity across the catalytic surface is uniformly decreased, and the loss of activity is linearly related to the amount of the poisoning agents chemisorbed. In the second case, the poison shows a preference for specific active sites if there are different types of sites on the surface. This way, the loss in activity is not a simple linear function of the amount of poison adsorbed. Moreover, although not always the case, the selectivity of the catalyst may also be affected if the poisoned active sites have a function different from the unaffected sites in the main reactions. Whether or not the selectivity of the catalyst is affected, and to what extent, depends not only on the nature of the poisoning agents and the catalyst but also on the reactions involved. For example, in the oxidation of methanol to formaldehyde, Ag can be poisoned by Fe or Ni, whereas in the conversion of ethylene to ethylene oxide, Ag is affected by the presence of C2H2. Coulson and Richardson (1994) have used the following equation to find the ratio of activity of the poisoned catalyst to the activity of the unpoisoned catalyst: F

1 tanh 冢 1 冣 tanh

(5.445)


513

where: F the ratio of activity of the poisoned catalyst to the activity of the unpoisoned catalyst the fraction of active site poisoned Thiele modulus for a first-order reaction, which takes place in a flat plate of catalyst. There are two limiting cases: (a) If is very small, F becomes equal to (1 ) and the loss in activity is linearly connected to the amount of poison adsorbed, and (b) If is high, then F becomes 兹苶 1苶 and the activity is less affected than in case (a) by the poison adsorbed because large means that the reactants have more difficulties in penetrating the interior of the particle. 5.4.2 Fouling and coking Although coking and fouling are often used interchangeably, they are not exactly the same. Fouling is a more general term that covers all the cases where the active sites are blocked, or generally, covered physically by a deposit (masking or pore blockage). For example, Pb particulates physically block the pores inside the catalyst, if present in the feed, besides the fact that they react chemically with the active sites on catalytic converters, thereby deactivating the catalytic converter. However, the most common form of fouling is coking. Coking takes place when the surface reactions lead to the formation and deposition of carbonaceous residues, especially when hydrocarbons are involved in the catalytic reactions. It is a mechanism of a physical nature. The coke produced and deposited on the catalyst may amount to 20% of the catalyst weight. For example, extensive coking may occur during the wet oxidation of phenol aqueous solutions in the presence of a copper oxide catalyst and can lead to the deactivation of the catalyst within several hours or days, depending on the specific experimental conditions. These deposits responsible for fouling can block out the reactants and prevent them from reaching the active sites, or even block the internal pores of the catalyst. Hydrocarbons and aromatics are usually the cause of coking. The chemical nature of the carbonaceous deposits relies on many parameters: temperature, pressure, feed composition, nature of products, and catalyst age share the responsibility of the residue formation on catalysts. Catalysts deactivated by coking can usually be regenerated if treated properly at high temperatures so that the carbon is burned off. This way, the initial activity can be totally, or to an extent, restored. The amount of coke deposited on the catalytic surface has been correlated to the time on stream (Voorhies, 1945): CC At n

(5.446)

where: CC t A n

the %wt of coke on the catalyst time on stream fouling parameter which is a function of feedstock and reactor type fouling parameter, typically close to 0.5.

514

5. Catalysis

This equation should be applied if the carbon deposited does not depend on the feed rate. However, this equation has been widely accepted and used, even beyond the range of its normal application. Froment and Bischoff (1990) have developed another approach to include the effect of coking, considering the composition of the feed, catalyst temperature, and activity. They assumed that coke formation follows a reaction parallel or consecutive to the main reaction scheme: AB A C or A B C where A is the reactant, B the product, and C the carbon formed. Considering the common reaction A ↔ B step first, the following expression for the rate of reaction was presented, assuming that the surface reaction is rate-controlling: C   kr Ct K A A  CA B   K r 1 K A CA K BCB

(5.447)

where r represents the reaction rate, Ct is the concentration of the active sites available to the main reaction, and A the remaining activity function, defined as A

(Ct CC* ) Ct

(5.448)

Here, CC* is the concentration of the active sites covered by coke. The authors presented empirical relations to connect A with the coke content of the catalyst CC: A e (Cc )

(5.449)

1 1 CC

(5.450)

or A


515

The constant is a function of the operating conditions and its use should be limited within the range of conditions for which it has been estimated. For estimating the reaction rate, the relation of CC with time has to be determined. If the formation of coke occurs in parallel with the main reaction, the following equation is applicable: rC

kc Ct K A CCA 1 K A CA K BCB

(5.451)

(Ct CC* ) Ct

(5.452)

where C

Following a similar approach, a rate equation in the case of the consecutive scheme can be also derived. Various reports can be found in the literature in connection with catalyst deactivation kinetics (Wojchiechowsky, 1968), some of them also taking into account the effects of diffusion resistance (Beeckman and Froment, 1980). 5.4.3 Thermal degradation Continuous exposure of catalysts to high temperatures may cause an alteration in its components and gradually lead to its deactivation. Thermal degradation may have an undesirable impact on both the catalyst substrate and noble metal load in various ways. Thermal degradation covers two phenomena: sintering and solid-state transformation. “Sintering,” also termed “aging,” is the loss of catalytic action caused either by the loss of catalytic surface area due to crystal growth, or by the loss of washcoat area due to a collapse of pore structure, as a consequence of the exposure of the catalyst to high temperatures. So, sintering proceeds via crystal agglomeration and growth of the metals on the support or by closing of the pores inside the catalytic particles. Sintering may occur in both supported and unsupported catalysts. In supported catalysts, sintering is considered to take place via agglomeration and coalescence of small metal crystallites into larger ones with lower ratios of surface to volume. In the case of unsupported catalysts, two different mechanisms have been proposed: (a) atomic migration, according to which metal atoms migrate from one crystallite to another via the surface (or in the gas phase), and thus the size of small crystallites decreases while that of the larger ones increase, and (b) crystallite migration, which involves migration of the crystallites along the surface of the support followed by collision and coalescence of two crystallites (Forzatti and Lietti, 1999). Metal-oxide catalysts and support suffer a decrease in the surface area and porosity upon exposure to high temperatures due to the coalescence and growth of the bulk oxide crystallites.

516

5. Catalysis

At very high temperatures, the chemical nature of the catalytic agents may be altered so that the catalytic activity is definitely lost. This type of thermal degradation is called “solid-state transformation” and can be seen as an extreme form of sintering, which leads to the transformation one crystalline phase into a different one. Phase transformations in the bulk washcoat and incorporation of an active metal into the washcoat may take place during solid-state transformation. Temperature is the main factor that affects sintering and solid-state transformation; experimental observations, however, have shown that the nature of the atmosphere in which the catalyst is heated may also play a part to an extent in the sintering process. For example, the presence of water vapor accelerates crystallization and structure modifications in oxide supports (Forzatti and Lietti, 1999). Various reports can be found in the relevant literature in connection with the kinetics of sintering of supported metal catalysts. A power-law equation used is (Forzatti and Lietti, 1999) d( D Do ) k ( D Do )n dt

(5.453)

where: D the metal dispersion at sintering time t Do the initial metal dispersion k the activated kinetic rate constant of sintering n the sintering order The metal surface area can be used in place of the metal dispersion. The variation of k with sintering time t and thus metal dispersion D has to be known for this equation to be applied. A variation of eq. (5.453) has been proposed by Fuentes et al. (1991) for quantitatively determining the effect of temperature, time, and atmosphere on the sintering rate of supported metal catalysts:  D Deq  d( D Do ) k  dt  Do Do 

n

(5.454)

The symbols used are the same as those used in the previous relation. The term Deq/Do has been added in eq. (5.454) to account for the asymptotic approach observed in the typical dispersion versus time curves (Forzatti and Lietti, 1999). 5.4.4 Other mechanisms of deactivation Besides the three main deactivation mechanisms described above, there are also some other ways of catalyst deactivation. At elevated temperatures, a loss of catalytic activity may be the result of volatilization. Direct metal loss through volatilization is rather


517

negligible. However, metals may form volatile compounds such as oxides and be lost in the effluent gas. Attrition may lead to loss of catalytic activity due to loss of catalytic material. It can be a cause of partial deactivation in moving and fluidized beds. Moreover, at high velocities and during sharp changes of temperatures, washcoat material on monolith honeycomb catalysts may be lost. 5.4.5 Regeneration of deactivated catalysts As already discussed, if a catalyst is deactivated via coking, it could be regenerated so that its initial catalytic activity is restored. The treatment at high temperatures in an oxygenrich atmosphere can burn off the coke deposited and the catalyst may regain its activity. Moreover, if the reduced activity is a temporary event caused by an inhibitor, the removal of the inhibiting substance of the feed can restore the catalyst to its initial potential. However, in most cases of poisoning, fouling, sintering, solid-state transformation, volatilization and attrition, the loss of activity is irreversible and definite, and in the best scenario only a fraction of the initial activity can be restored. Three-way catalytic converters, which constitute the widest application of catalysts in our life, are manufactured for prolonged operation under hard and constantly varying conditions. However, although at slow rates, they are progressively deactivated irreversibly through various ways: components in fuels and lubricants lead to poisoning, high temperatures lead to sintering, or loss of material results from attrition. Generally, in the case of automobile catalytic converters, deactivation can be attributed to poisoning or thermal aging of catalyst. Poisoning: It refers to the loss in catalytic activity due to the occupation of active sites on the catalyst by substances via chemisorption. The main poisonous substances for catalytic converters are Pb, Zn, Ca, P, S, Si, and other metal compounds. The elements Pb and S come from the fuel, P, Zn, and Ca from the lubricant, whereas the metallic elements (Fe, Ni, Cu, and Cr) come from the metallic parts of the engine and the exhaust system. Although Pb has been eliminated in fuels, it can be found in unleaded gasoline at a level of 0.013 g/L, due to its presence at various stages of gasoline production. For temperatures up to 600 °C, in the presence of sulfur compounds, PbSO4 is formed, which is physisorbed on catalytic sites (reversible poisoning). PbO2 is formed for temperatures above 700 °C, which on being chemisorbed on catalytic sites causes irreversible poisoning. P present in oil additives may also enter exhaust emissions through unburned oils and may form compounds that can reversibly or even irreversibly poison the catalyst. Finally, S in fuel forms H2S and oxides, which decrease the catalytic activity. Thermal aging: Continuous exposure of the catalytic converter to the high temperatures of exhaust gases may influence the active components and/or the catalyst structure, gradually leading to its deactivation. The catalyst may be affected by thermal ageing in many ways. For operation temperatures above 600 °C, Rh2O3 reacts with alumina to form inactive Rh2Al2O4 (Forzatti and Lietti, 1999), whereas above 700 °C, Pt particles condense, thus becoming larger (20 nm). This process may take place in various ways, such as crystallite migration and incorporation, via the emission of single atoms from small crystallites and their capture from larger ones, or through a combination of these mechanisms. Above 900 °C, condensation of -Al2O3 and alloying of noble metals may take place. A PtRh alloy

518

5. Catalysis

is formed and Pt particles are covered by Rh oxides. At even higher temperatures, -Al2O3 is condensed extensively and transforms into -Al2O3 or -Al2O3, and as a result its specific area decreases from 150 m2/g to 50 m2/g or even less. The formation of -Al2O3 is accompanied by mechanical tensions that lead to substrate cracking and noble metal loss. Thermal deactivation is normally irreversible, although redispersion of the sintered metal surface is possible (Angelidis and Papadakis, 1997; Poulopoulos and Philippopoulos, 2004). 5.4.6 Deactivation of catalysts in liquid-phase applications Although catalysts have been extensively used for the abatement of gas-phase pollutants, their application in water-phase processes for environmental purposes is a relatively novel subject with tremendous potential in the near future. However, catalyst durability and activity in such applications have to be definitely improved (Pirkanniemi and Sillanp, 2002). Unfortunately, in liquid-phase applications, catalysts experience severe deactivation and may suffer great loss of potential at rates much higher than those in gas-phase operations. Besides poisoning, sintering, and the other deactivation mechanisms discussed above, leaching may also be another cause of deactivation. Hot and acidic media promote solubility of some metal oxides and enhance deactivation by leaching of metal or metal-oxide catalysts (Matatov-Meytal and Sheintuch, 1998). Whatever the reason for deactivation, it is dependent on the catalyst, support, and treated water. It has to be noted that in liquid applications, the pollutants concentrations faced by the catalyst are generally much higher than those found in gas treatment, let alone the presence of various poisoning agents in wastewaters. For example, noble metals are highly sensitive to poisoning if halogen-, sulfur-, or phosphorus-containing compounds are present in reaction media (Pirkanniemi and Sillanp, 2002). For example, the most noteworthy disadvantage of catalytic wet oxidation is the severe catalyst deactivation (Larachi et al., 1999). Hamoudi et al. (1998, 1999) systematically studied the deactivation of MnO2/CeO2 catalyst during wet catalytic oxidation of phenol and the catalyst-surface modifications. It was observed that deactivation was induced mainly by the formation of carbonaceous deposits on the catalyst surface. Ohta et al. (1980) reported that the size of the catalyst particles affected the stabilization of catalytic activity. For granular particles of supported copper oxide, the catalytic activity was decreased after each run, even after six successive experiments. In contrast, for larger particles the catalytic activity was stabilized after the first three runs. Obviously, a lot of work has to be done in order to elucidate the deactivation mechanisms of catalysts in liquid-phase processes and how their span can be prolonged. 5.4.7 Kinetics of catalyst deactivation The gradual loss of catalytic activity adds more to the existing complexity of catalytic systems. Thus, it has to be taken into account during the modeling of such systems. During the presentation of each deactivation mechanism, some kinetic models were given. Here, the


519

more general approach of Szepe and Levenspiel (1971) will be discussed. They developed a phenomenological treatment to cope with the problem of deactivation generally. Following this approach, the activity of a deactivating catalyst is expressed as

r ro

(5.455)

where:

the activity of the catalyst r the rate of reaction after the time on stream ro the rate of the fresh catalyst Obviously, the rate of reaction decreases with time for a deactivating catalyst, and thus the activity of the catalyst is also a function of time: is initially equal to 1 and declines with time, approaching zero for a totally deactivated catalyst. In general, the reaction rate depends on the reaction conditions as well as on the catalyst activity. The latter is in turn a function of the past of the catalyst. So, r f (C,T,P,, )

(5.456)

In analyzing reactions over deactivating catalysts, we can follow separable or non-separable kinetics. According to separable kinetics, Szepe and Levenspiel used two different terms: one for the reaction kinetics (independent of time) and another for activity (time dependent). Consequently, r ro (C , T , P,)(t )

(5.457)

where 0 1. Since the decline of catalytic activity is owing to the decrease in the number of active sites, should be correlated to the fraction Nt/No, where Nt is the number of active sites after deactivating time t and No is the number of active sites of the fresh catalyst. This correlation is derived from experimental observations, or an empirical form is used (Forzatti and Lietti, 1999). Consider the dehydrogenation conversion of methyl-cyclohexane (A) to toluene (B) with coke (C) formation: 3 1 2 A l ← → Aⴱ ← → Bⴱ ← → B l k4 C

k

k

k

Butt and Petersen (1988) extended the LangmuirHinshelwoodHougenWatson kinetics to involve the varying activity (as a result of catalyst deactivation with time) to describe

520

5. Catalysis

the kinetics of the reaction scheme above. Assuming that (a) the surface reaction A*↔B* controls the rate, and (b) the poisoning rate is by far smaller than the reaction rate, they derived the following expression of rate (Forzatti and Lietti, 1999):

r

 t  k 2 K A C A Ct k4 K A CA exp ∫ dt 0 1 K A CA K BCB 1 K A CA K BCB  

(5.458)

If the concentrations of the reactants do not affect coke formation, eq. (5.458) can be simplified to r

k 2 K A C A Ct eK t 1 K A CA K BCB

(5.459)

where K ∫

t

k4 K A CA A C A K BC B

0 1 K

(5.460)

It has to be noted that the separability requirement cannot always be applied. Depending on the reactiondeactivation network and the approach followed (separable or nonseparable kinetics) various expressions have been derived (Carberry, 1976). 5.4.8 Management of spent catalysts In industry, some catalysts can be regenerated for reuse so that the operational cost associated with catalyst supply is minimized. However, in most cases as presented above, and in the case of automotive catalysts, the catalytic activity cannot be restored and the catalyst is then spent. Specifically, catalysts are typically in the form of a ceramic support carrying small amounts of metals such as chromium, nickel, or platinum. Alumina and silica are commonly used in the construction of the ceramic support. The catalysts lose their activity progressively via various deactivation mechanisms (Pavel and Elvin, 1994). Thermal regeneration is often employed for regaining catalytic activity, if applicable, but some of the particles break during this process. Once the catalyst particles become too small to be useful, they constitute a waste disposal problem, since catalysts may contain heavy metals that are considered hazardous, or other harmful components. In the context of sustainable development, all spent catalysts should be recycled as far as possible, or properly disposed off in an environmentally friendly manner, in case recycling is not possible (NFESC, 1996). Catalysts contain precious metals and their recovery nowadays is recognized to be of great importance because


• • •

521

precious metals that can be further used in various processes can be saved, energy is saved, the environment is protected from the accumulation of harmful materials

Nonhazardous spent catalysts can be also reused in the production of bricks. Specifically, catalysts are crushed and decreased in size to form alumina/silica sand that can replace the sand used in the manufacture of bricks. Moreover, spent fluidized-bed catalysts can be reused as cement components. Specifically, the catalyst is used to replace clinker in the final grinding (Cardenosa et al., 1992). For the disposal of catalysts, the techniques presented in Section 4.3 can be largely applied.

–6– Reactors Scale-up

The procedure during which a physical or chemical process is transferred from the laboratory or pilot scale to the commercial one is called scale-up. The term “scale-up” should not be understood only in the sense of increasing the dimensions while preserving similar systems. Often, the transposition of one system to another offers a better solution to a problem (e.g. a pilot plant operating in upflow for industrial unit operating in downflow) (Trambouze, 1990). While the rate of a given chemical reaction is independent of the size and the reactor structure, the physical processes involved in the overall rate, for example, mass and heat transfer, are usually controlled by these factors (Perry and Green, 1999). Furthermore, scaleup stands a much better chance for success if the laboratory and large-scale units are carried out in the same type of system, for example, fixed-bed, fluidized-bed, and batch reactors (Smith, 1981). From this point of view, keeping the same reactor type and thus the same reactor structure at different scales, the critical parameter is the reactor size, which incorporates its geometrical analogies and dimensions. Furthermore, in the case of adsorption and ion exchange in fixed beds, the fact that neither chemical reaction nor heat transfer occurs is crucial as these operations are essentially isothermal. Thus, scale-up mainly takes into account mass transfer phenomena, and of course the hydraulic performance of the reactor (liquid distribution, small-scale dispersion of the flow and liquid holdup). Scale-up is limited by several factors, such as maximum working velocity in the case of adsorption/ion-exchange upflow fixed beds, and maximum allowable temperature in cases where microorganisms are involved in the process, for example, in the packed bed bioreactors (Michell et al., 1999). 6.1 GENERAL ANALYSIS OF THE SCALE-UP PROBLEM Various methods can be employed in modeling chemical or physical processes. There are three basic approaches in the scale-up procedure (Mukhyonov et al., 1979): (a) mathematical modeling, (b) physical modeling, and (c) experimental scale-up. 6.1.1 Mathematical modeling The first stage in the mathematical modeling of a process is to express mathematically the relationship between the basic process variables and the boundary conditions and set up an 523

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6. Reactors Scale-up

algorithm. The second step consists of changing the various parameters, using the algorithm, to check whether the model corresponds to the process investigated and to select the optimal conditions. There are various advantages to the mathematical modeling method, such as deeper process understanding, more scale-up factors included, few reactor scales, and design flexibility. In complex systems such as three-phase reactors, the methods of mathematical modeling cannot provide the required information for process design and scale-up since it is practically impossible to take into account all existing phenomena and safely predict the influence of hydrodynamics, heat and mass transfer, or kinetics on each other (Datsevich and Muhkortov, 2004). Thus, models are almost always approximate in nature. They are based on a number of assumptions that cannot be met during scale-up. So, it is not surprising that industrial unit designers do not completely trust the results obtained from mathematical modeling. Thus, several systems cannot be fully modeled mathematically and other methods for scale-up are followed. 6.1.2 Physical modeling Physical modeling is an alternative to the mathematical one when the latter is not applicable. Two different methods can be followed in the context of physical modeling: (a) the similarity method, and (b) the dimensional analysis. However, in practice, these two methods are combined into one. The most important method is the similarity method first proposed by Newton. In fluid dynamics, three types of similarity can be considered (Mukhlyonov et al., 1979; Treybal, 1980; Holland, 1962): •

•

•

Geometrical similarity: Two systems exhibit geometrical similarity when the ratios of corresponding dimensions in one system are equal to those of the other system. Hence, between two pieces of equipment of different sizes, geometrical similarity exists only when they both have the same shape. Kinematic similarity: This type exists between two geometrically similar systems of different sizes when the ratios of velocities between corresponding points in each system are the same. Dynamic similarity: If two systems exhibit the same ratio of forces between corresponding points, in addition to being geometrically and kinematically similar, they also exhibit dynamic similarity.

The definition of dimensionless numbers has been given in a simple way by Noble and Terry (2004): A group of physical quantities with each quantity raised to a power such that all the units associated with the physical quantities cancel, i.e. dimensionless. So, the classical principle of similarity can be expressed by equations of the form Q ⫽ f ( R, S,...)

(6.1)

6.1 General Analysis of the Scale-up Problem

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where the dimensionless group Q is a function of other dimensionless groups. Dimensional analysis has to be conducted for each case so that the above equation is derived. It is a way for expressing the behavior of a physical system in terms of the minimum number of independent variables. Every dimensionless group represents a rule for scale-up. It is possible that one scale-up rule is contradictory to another one. Hence, it is a usual tactic during scale-up to deliberately suppress the effects of certain dimensionless groups in favor of a particular dimensionless group (Holland, 1962). Physical modeling is not as accurate as mathematical modeling. This should be attributed to the fact that in dimensionless equations, the dependent number is expressed as a monomial product of the determining numbers, whereas the corresponding phenomena are described by polynomial differential equations. Moreover, errors in the experimental determination of the several constants and powers of the dimensionless equations can also lead to inaccuracies. We should also keep in mind that the dimensionless-number equations are only valid for the limits within which the determining parameters are varied in the investigations of the physical models. The following dimensionless numbers are often employed (Mukhyonov et al., 1979): • •

• •

Geometrical dimensionless numbers: They are expressed by ratios of several linear dimensions (height, diameter of the reactor, particle size, etc.) of the apparatus used. Hydrodynamic dimensionless numbers: Examples are the Reynolds number, Froude, Archimedes, and Euler number. These dimensionless numbers have to be functions of identical determining dimensionless numbers of the same powers and with the same value of the other constant coefficients, so that the model and the object are similar. Diffusional dimensionless numbers: The Peclet, Prandtl, Schmidt, Sherwood, and Nusselt number are the most common ones. Different kinetic dimensionless numbers: These can also be used depending on the conditions of the process. The most important is the Damköhler number.

In its simplest application, the principle of similarity attempts to represent a process by an unspecified relation between several dimensionless groups, one of which contains the unknown variable. If the groups containing the known variables are forced to have the same value on both the small and large scale, then the group containing the unknown variable will also have the same value (Edgeworth and Thring, 1957). In this form, the principle of similarity presupposes that the systems to be compared are geometrically similar. There are two methods of deriving similarity criteria: (a) empirical dimensional analysis, and (b) dimensional analysis using the differential equations (Edgeworth and Thring, 1957). When applicable, the second method is to be preferred. Empirical dimensional analysis To obtain the dimensionless groups for a specific process, the so-called “Buckingham Pi theorem” is frequently used. The first step in this approach is to define the variables that affect the process or assume the most important physical parameters for the specific process, if the equation that describes the process is entirely unknown. This is the weak

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point of this method since it is difficult to have at hand these “significant” parameters without having a model for the process. The theorem states that the number of dimensionless groups Pi is equal to the number of independent variables n minus the number of dimensions m. Then, each dimensionless group can be expressed as a function of other groups. In most cases, the exact functional form comes from experimental studies. The basic dimensions are length L, time T, and mass M. An independent variable is a variable that cannot be a function of the other involved variables. For example, kinematic viscosity is a function of density and dynamic viscosity. In this case, two of these three variables can be considered as independent. Example 1 Consider the mass transfer across a flat plate. In this case, the important variables are (dimensions in parentheses): the mass transfer coefficient k (L/T), the bulk fluid velocity u (L/T), the kinematic viscosity of the fluid (L2/T), the solute diffusion coefficient D (L2/T), and the plate length l (L). The number of independent variables n ⫽ 5 and the number of the involved dimensions m ⫽ 2. Hence, the number of dimensionless groups Pi ⫽ n ⫺ m ⫽ 3. Next, we select two of the variables that do not form a dimensionless group. These variables could be and l. Then, by combining these two variables with one more of the remaining three, we attempt to form three dimensionless groups by raising these two variables to the appropriate power, the aim being that all the units associated with the physical quantities are cancelled: l a b k ⫽ La

L2b L Tb T

Consequently, the following equalities should be satisfied: for L : a ⫹ 2b ⫹1 ⫽ 0 for T : ⫺b ⫺1 ⫽ 0 By solving this set of equations, we obtain a ⫽1 b ⫽⫺1 So, the first dimensionless group is P1 ⫽

kl

Repeating this procedure for D and u, the result is D ul P3 ⫽ P2 ⫽


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Finally, P1 ⫽ f ( P2 , P3 )

kl ⫽

 D ul  f ,   

In this example, we recognize two well-known dimensionless groups, i.e. the Reynolds (Re ⫽ ul兾v) and Schmidt (Sc ⫽ v兾D) numbers. In contrast, the third dimensionless group in the last equation is not usually used. Instead, the Sherwood number is more useful (Sh ⫽ kl兾D). The Sherwood number can result from multiplying both sides of the original functional form with the Sc number. The final relationship is Sh ⫽ f ( Re, Sc) These groups have a definite, important, physical meaning. The Reynolds number is the ratio of inertial forces to viscous forces, the Sherwood number the ratio of mass transfer resistance in fluid film to mass transfer in bulk fluid, and Schmidt number the ratio of momentum diffusivity to mass diffusivity. Edgeworth and Thring Method Another way to derive the dimensionless groups needed for scale-up purposes has been proposed by Edgeworth and Thring (1957). The general concept is that the differential equations that describe the process could be used, since they are known for most processes that are relevant to chemical engineering. The main problem is that many of these equations cannot be easily integrated or are very difficult to be handled, in general. Provided that these differential equations are dimensionally homogeneous, as all complete physical equations should be, they can be simplified to a generalized dimensional form by dropping the differential signs and substituting for linear coordinates a generalized linear dimension L. Dividing across by one term, one finds the relevant dimensionless groups. Following this procedure, from a complete set of differential equations, there is no doubt as to whether the correct variables have been included. Example 2 Consider the Navier–Stokes differential equations for the isothermal flow of a Newtonian viscous fluid:  u u u u  x ⫹  ux x ⫹ uy x ⫹ uz x  ⫽ g cos a x  x t y z   2 ux 2 ux 2 ux  p ⫺ ⫹  2 ⫹ 2 ⫹ 2  x y z   x In addition, there are two more identical equations for the y and z axes. In the equation the symbols “ux”, “uy” and “uz” denote the velocity constituent in x, y and z axis respectively. The corresponding generalized dimensional form of this equation is 2  p         t  ⫹  L  ⫽ 关g 兴 ⫺  L  ⫹  L2   

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Dividing across by 2/L, we obtain  Lg   p    L  t  ⫹1 ⫽  2  ⫺  2  ⫹  L      For steady flow, L/t drops out. Rearranging and omitting the plus and minus signs,  L 2  p ⫽ f  , Lg  2 This is the well-known dimensionless equation for fluid flow. It represents the pressure coefficient as an unspecified function of the Reynolds and Froude groups. In geometrically similar flow systems, the pressure coefficients will be also equal, provided that the Reynolds and Froude numbers are both equal. The principle of similarity requires geometrically similar systems to be compared at equal values of the appropriate dimensionless groups, called “similarity criteria.” However, in many cases, this requirement is not possible to be fulfilled, since it may be either impractical or impossible to be achieved. To overcome this problem, the method of extrapolation has been proposed. It is found that in many cases, the unspecified function can be quite well represented by a power function. For example, for closed fluid-flow systems, the Froude group is negligible and thus  L  p ⫽ c   2

where c and a are known constants. The method of extrapolation is used to predict the performance of the large-scale apparatus at an equal value of the similarity criterion and/or extrapolate this performance to a different value of the similarity criterion assuming a powerlaw relation with an exponent either determined experimentally or taken from the literature. Whether or not the fundamental differential equations governing a process are known, it is essential to discover the rate-determining factor or regime of the process, before any prediction can be made about the effects of changing of scale. The three broad types of regime that are met in chemical engineering are dynamic, thermal, and chemical. The prevailing regime must not be confused with the nature of the process itself. For example, a chemical reaction whose rate is limited only by the rate at which the reactants can be brought together by mixing, is subject to a dynamic regime. Where no complete mathematical description of the process and no dimensionlessnumbers equations are available, modeling based on individual ratios can be employed. This is the most characteristic case for a number of industrial processes, especially in the field of organic-chemicals technology. This method is referred to as “scale-up modeling” (Mukhyonov et al., 1979). In such cases, individual ratios for the model and the object, which should have a constant value, are employed. For instance, there should be a constant ratio between the space velocity of the reacting mixture in the model and the industrial object. Some of the dimensionless numbers mentioned in physical modeling are also employed in this case.


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6.1.3 Experimental scale-up Experimental scale-up can be seen as part of physical and scale-up modeling. In the following, its methodology is briefly presented. Collect and analyze data A carefully planned test campaign is needed to collect data that adequately cover a wide range of the most important operating variables, conducting the minimum possible experiments. These laboratory-scale experiments should be held in a wide range of conditions that cover those expected in the commercial units, for five key operating variables (Dutta and Gualy, 2000): space velocity, temperature, pressure, composition, and particle size. Initially, only one variable should be changed at a time, keeping all the others constant, if possible (Dutta and Gualy, 2000). Temperature is usually expected to play the principal role in the reaction kinetics of most systems. Therefore, most of the experiments have to be connected to the effect of that variable. For reactions involving solids, catalytic or noncatalytic, both solid particle size and temperature should be varied in such a way that the data obtained represent both the absence and the presence of significant pore-diffusion phenomena. Then, the next step is the selection of the appropriate rate expression form and, subsequently, the rate parameters should be estimated by minimizing the differences between the values predicted from the selected rate expression and the data experimentally found. At this point it is important to consider safety. Safety issues are obviously of paramount importance and have to be dealt with adequately, especially in exothermic reactions. Specifically, laboratory and pilot reactors are usually made of small-diameter tubes and therefore effective heat removal can be achieved. On the other hand, commercial reactors have large diameters, approaching adiabatic conditions, and heat build-up may appear (Dutta and Gualy, 2000). Define reactor type and its hydrodynamics The reactor type is defined by the • • •

physical configuration of the volume occupied by the reaction system, flow mode of various streams in and out of the reactor, hydrodynamic representation of the flows within the reactor volume.

The relative importance of major phenomena that may affect reactor models are shown in Table 6.1. Working with test reactor units A proper laboratory or process development unit (PDU) is required if there is a lack of information on the reaction mechanism, kinetics, and the reactor hydrodynamics, especially for a new reaction system (Dutta and Gualy, 2000). In laboratory experiments, certain aspects of the process are investigated by handling small amounts of raw materials to reduce the material constraints to a minimum. In these experiments, all mechanisms that do not depend on size, such as thermodynamics and chemical kinetics, can be illuminated (Trambouze, 1990).

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Table 6.1 Relative importance of major phenomena that may affect reactor models (Dutta and Gualy, 2000) Phenomenon

Usually more important

Pore-diffusion resistance

Reactions involve solid particle Reactors with particle size lower size greater than about 1.6 mm than 100 m to 0.1 mm All fast, noncatalytic gas–solid Catalytic bubbling fluidized (G/S) reactions such as combusbeds (BFB) Slurry reactors tion and gasification All bubbling reactors such as Catalytic fixed-bed G/S reactors BFB and three-phase reactors All fast, noncatalytic G/S reactions such as combustion and gasification Fixed-, moving-bed, and BFB G/S Entrained-bed reactors reactors Liquid-phase reactors Generally all deep beds Across two-phase interface in fast Within solid particles in reactions Gas side of tube wall in solid–fluid reactions liquid-cooled gas-phase or G/S reactors Small-diameter laboratory and Commercial units pilot plant reactors Low Z/D and low Reynolds High Z/D and high Reynolds number flow conditions flow in open pipes Vessel with baffles or internals obstructing flows Large-diameter reactors with (Usually ignored in low flow rates preliminary models) Fixed and moving-bed G/S reactors Dense phase of BFB reactors

Film diffusion resistance

Pressure drop

Heat-transfer resistance

Heat loss to atmosphere Axial dispersion

Radial dispersion Temperature profile

Usually less important

First, some experiments should be conducted with the solids in the form of fine powders so that the pore-diffusion resistance is minimized and the intrinsic kinetic is revealed. Second, PDU should mimic as closely as possible the design, hydrodynamic conditions, and operation, including the solids particle size, expected in the commercial unit. Unfortunately, a close approach to the commercial system is not often possible in a labscale PDU. Therefore, an extensive pilot-plant study is usually undertaken. Pilot-plant experiments are realized at a scale that has wide variations, but in which all the industrial constraints are taken into account. It is during pilot-plant experiments that scale-up problems must be dealt with adequately. Representative operation implies operation that is identical or transportable by using a mathematical model (Trambouze, 1990). The pilot plant is thus a tool for the physical as well as chemical mechanisms. In particular, it is indispensable for measuring the extent of the possible interactions between the two types of mechanisms. In designing a pilot plant, the most important question is how large to make it, and unfortunately there is no a single answer to this question (Knowlton, 2000). If the pilot plant is too small, there will be problems, such as wall effects and slugging in fluidized beds. On the other hand, if it is too large, the cost could be too high.

6.2 Practical Scale-up Rules for Certain Reactor Types

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All chemical processes regardless of type involve various mechanisms in addition to the desired chemical conversion, such as chemical reactions, thermodynamic, physical, and chemical equilibria, heat transfer, and mass transfer, which are not independent from one another, thus making it difficult to study their interactions. For example, transfer phenomena essentially depend on fluid flow. In other words, the scale or size of the equipment in which the process takes place has a different effect depending on the mechanism concerned. Extrapolation using geometric similarity can be proved extremely useful in determining the effect of size on a number of characteristic magnitudes of the system. This is shown in Table 6.2. The aspect that is mainly affected by size is the physical behavior of the system. Therefore, by eliminating the chemically related terms from the process model, the physical aspect can be analyzed separately and adjusted appropriately by conducting specific experiments that exclude the chemical behavior of the system. This leads to the design of mock-ups. These PDUs are called “cold models” due to the absence of chemical reaction and may be needed to assess the hydrodynamics of a totally new reactor configuration. These cold models allow us to examine the impact of size on physical phenomena such as hydrodynamics separately (Trambouze, 1990). The mock-up simulates the physical process by adapting mild operating conditions such as ambient temperature and atmospheric pressure and by using inexpensive and harmless fluids with physical properties approaching those of the reaction fluids. The simulation, which is based on the theory of similarity, makes use of dimensionless numbers characterizing the major physical mechanisms. 6.2 PRACTICAL SCALE-UP RULES FOR CERTAIN REACTOR TYPES 6.2.1 Fixed beds Adsorption and ion exchange Under specified conditions, the data of the laboratory-scale unit can be used for the evaluation of the large-scale unit performance. The similarity rules can be drawn using

Table 6.2 Influence of size on a number of key mechanisms in chemical processes Mechanism

Important variables

Influence of size

Chemical kinetics Thermodynamics Heat transfer Mass transfer within a fluid phase Transfers between phases

Temperature, concentration, and pressure Temperature, concentration, and pressure Local velocities, concentration, and pressure Temperature, concentration, and relative velocities of phases Temperature, concentration, and relative velocities of phases Flow rates and geometry Temperature, concentration, pressure, and geometry

None None Indirect Indirect

Forced convection Free convection

Indirect Important Determining

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the continuity equation, the rates equations, and the hydraulic equations (for %h and Ped). From the continuity equation (4.129), DL 2C C Z q Z C ⫺ ⫺ b ⫽ 2 ( z ⲐZ ) us Z ( z ⲐZ ) us t us t

(6.2)

it is clear that for the same ion-exchange or adsorption system and the same Co and qmax, the exit concentration C at each time interval t will be the same for any bed size provided that the equation terms are the same. These terms are shown in Table 6.3.

Table 6.3 Parameters derived from the analysis of the design equations Parameter

Equation

Parameter is a function of

Z/us PeL Ds r (or dp) kf au h% Isotherm— equilibrium behavior

Continuity equation Continuity equation Continuity equation Rate equation for solid diffusion (4.130) Rate equation for solid diffusion (4.130) Rate equation for liquid-film diffusion (4.131) Rate equation for liquid-film diffusion (4.131) Rate equation for liquid-film diffusion (4.131) Liquid holdup correlations Isotherm

D/dp — Ped, Z/dp, or equally, Z/dp, Rep — — and Rep dp, , and particle shape D/dp us, dp, Maybe influenced by contact time (Z/us)

Note: The parameter b is found in the continuity equation but is behavior eliminated when the rate equation is incorporated in its expression.

Table 6.4 Comments on the critical design parameters Parameter

Comments

Z/us () Z/dp D/dp Rep

Critical Minimal effect if higher than 150 Minimal effect if higher than 30 Minimal effect if solid diffusion is the controlling mechanism and the bed is operated in near-plug-flow mode (or for Z/dp higher than 150) Critical Minimal effect if higher than 10 (dp/D ⬍ 0.1) Minimal effect if solid diffusion is the controlling mechanism and the unit is operated in upflow mode. The same holds for downflow mode if the liquid holdup is 100%.

dp us


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Under some circumstances, the parameters in Table 6.3 may not have the same influence on the bed performance. In Table 6.4, some critical comments are summarized. Thus, in fixed beds, the critical parameters are the contact time and particle size. Particle size should be kept the same, since it is well known that it affects the rate of adsorption and ion exchange. However, the role of particle size is more complicated because it also affects the pressure drop. For this reason, it is desirable to use a narrow particle size range during design, as the large particles control the uptake rate, whereas the small ones control the pressure drop (McKetta, 1993). As a rule, it is not possible to keep the linear velocity constant during scale-up., The linear velocity can be similar in both units only if the pilot plant has a very high height-todiameter ratio compared to the large unit. However, contact time is more critical than superficial velocity. Contact time is incorporated into the continuity equation, representing the physical time allowed for the contact of the phases. The physical meaning of this parameter is the time allowed for the “reaction.” However, it is known that in some systems contact time has a possible impact on the equilibrium state, leading to “partial” equilibrium behavior in the bed, at least in liquid-phase adsorption and ion-exchange processes that are reversible and of low kinetics (Inglezakis and Grigiropoulou, 2003). It is important note that this effect is essentially another “nonideality,” which is not incorporated into the continuity equation. Thus, scale-up should be based on the same contact time and particle size. Contact time (or space time) is determined by linear velocity as ⫽

V Z ⫽ o ⫽ Qrel Q us

(6.3)

where Qrel is the relative volumetric flow rate, commonly expressed in empty bed volumes per hour (BV/h). Qrel is equal to the reactor space velocity: Qrel ⫽

Q us ⫽ Vo Z

(6.4)

and the basic scale-up rules are  Z   Z  FS ⫽ LS   ⫽    us  FS  us  LS

(6.5)

(dp )FS ⫽ (dp )LS

(6.6)

where subscripts “LS” and “FS” denote lab-scale and full-scale units. From the hydraulics perspective, if scale-up is based on the same superficial velocity, PeL will be higher in a large bed in downflow operation due to the higher bed height, whereas the liquid holdup will be low due to the low velocity, which is frequently used in laboratory beds. This leads to problems and special efforts are required to improve the liquid holdup, for example, a special distributor design. These problems are absent in upflow operation. On the other hand, keeping the same contact time, the linear velocity is much higher in a large column, which is not necessarily a problem, since velocity influences the controlling

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step (mass transfer in liquid film) and hydraulics (maldistribution and liquid holdup). In a large column, higher velocity leads the system to solid diffusion control, higher liquid holdup (for liquid–solid systems and downflow operation), and higher PeL (closer to plug flow), provided that the fluid dispersion at the inlet of the column has been well designed. In such a case, better results are expected, i.e. higher breakthrough volume will be achieved due to the fact that the liquid-film resistance can be minimized, and for downflow operation, liquid holdup (for liquid-solid systems) and PeL are high enough. Adsorption from the liquid phase using porous solids is frequently controlled by the solid diffusion step. From this point of view, the superficial velocity is expected to have a minimal impact on bed performance, provided that plug-flow condition is approached and the liquid holdup (for liquid–solid systems) is near to 100%. Thus, if scale-up is based on the same contact time, the results obtained from the laboratory-scale bed can be directly used in the large one and the performance of the bed can be accurately evaluated. Consequently, if we have a contact time–breakpoint volume relationship derived from the laboratory bed experiments, we can use this relationship to evaluate the breakpoint volume of the large-scale unit for the specified contact time. In that case, when modeling is applicable to the laboratory-scale bed, a possible change in the controlling mechanism should be taken into account and a different model type is likely to be needed in the large-scale bed. From the above analysis, some important conclusions can be drawn: •

•

In the case of high fluid-film resistance, keeping the same contact time at the large unit, the higher the superficial velocity, the lower the fluid film resistance, and thus higher rates can be achieved at the large bed. However, for the same contact time, the operation in downflow mode is probably the best because high superficial velocities at large beds can lead to partial fluidization and particle attrition in upflow operation. Furthermore, a different model would probably be needed for each scale. In the case of low resistance in fluid film or/and unfavorable equilibrium (equilibriumlimited system), a high contact time at the large bed would be beneficial for the equilibration step, whereas a high superficial velocity is not expected to lead to better results, since the fluid-film resistance is minimal. Furthermore, the same type of model can be used for different scales.

During this part of the design, we should keep in mind that the same controlling mechanism in units of different size will permit the experimental data to be directly “transferred” from the small to the large unit. In most cases, the diffusion of molecules or ions within the solid matrix is the controlling mechanism, especially in micro- and mesoporous materials such as zeolites. The fluid-film diffusion resistance is inversely proportional to the linear velocity and is not important in most cases, because the linear velocity is increased in the diffusion film at a large-scale unit. Consequently, when the solid diffusion is the controlling mechanism at the laboratory bed, the change in linear velocity (under the same contact time) has no effect on the kinetics of the system. This allows the flow rate to be varied during scale-up. However, it is safe to consider that the adsorption rate will be higher in larger columns due to the increase in the linear liquid velocity, even when the controlling mechanism incorporates fluid-film resistance in small columns. In the case of liquid–solid systems, the contact time at the two scales will be the same only


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if the liquid holdup remains the same, and ideally close to 100%. Since liquid holdup is expected to be high enough in the large-scale column, provided that the liquid distributors are designed properly, especially in downflow operation, it is desirable to achieve a high liquid holdup in small columns also. However, this could be achieved only under the upflow condition. To have near-ideal plug flow in a small-scale bed, the particle Peclet number should be high enough to cause a high bed Peclet number, whereas in a large-scale unit this particle Peclet number is of minimal importance, since it is multiplied by Z/dp, and thus the bed Peclet number is expected to be high enough. Example 3 In the typical case of 2 mm particle size and a small bed of 20 cm height, the multiplier Z/dp is 100, whereas at a large bed of 200 cm height the multiplier Z/dp is 1000. Thus, the bed Peclet number is 10 times higher at the large column, provided that the particle Peclet number is similar in both beds. It should be noted here that the particle Peclet numbers may not be similar in both scales, since they depend on the flow condition (up- and downflow) and on the Reynolds number. However, it can be shown in the case of irregularshaped (zeolite-like) particles that the particle Peclet numbers are close enough for both flow conditions, in the range 4 ⬍ Re ⬍ 8.5. For spheres, this is valid in a broader range of Reynolds number, e.g. Re ⬍ 10 (see Section 3.6.3). To ignore the geometrical similarity between the different unit sizes, the following conditions should be satisfied (Carberry, 1976; Gunn, 1968): Z D ⱖ 5 ⱖ 30 D dp

(6.7)

Also, the scaling factor for the bed diameter should be kept equal to or lower than 10 (Peters and Timmerhaus, 1968). Using the above relationships, the limits of volumetric flow rates can be evaluated: Q ⫽ QrelV ⫽ Qrel ZA A ⫽

 4Q  Q D ⫽  Qrel Z  Qrel Z 

0.5

(6.8)

Then, the following equations hold: Z  4Q  D ⱕ  5  Qrel Z   4Q  D ⱖ 30 d p   Qrel Z   4QFS  DFS ⱕ 10 DLS   Qrel Z FS 

0.5

2

Z  Z  Q Z ⱕ Q ⱕ   rel  5 5 4

0.5

ⱖ 30 d p Q ⱖ (30d p )2

(6.9)

Qrel Z 4

0.5

ⱕ 10 DLS QFS ⱕ (10 DLS )2

Qrel Z FS 4

(6.10) (6.11)

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where the subscript “LS” denotes the laboratory-scale bed and “FS” the large unit. Using eqs. (6.9)–(6.11), the appropriate limits of working volumetric flow rates can be evaluated. Thus, the full-scale bed diameter can be estimated using eq. (6.8), according to the chosen working volumetric flow rate. Maximum working velocity and maximum bed height According to Ruthven (1984), the linear velocity should not be higher than a certain maximum value, so that extended friction between the packing material is avoided in both downand upflow operations. This maximum velocity is 0.8 times the minimum fluidization velocity for upflow operation and 1.8 times the same velocity for downflow operation. The minimum fluidization velocity can be estimated using the equations presented in Section 3.8.2. In Figure 6.1, the maximum linear velocityversus the particle diameter is presented. The following typical parameters are used: h ⫽ 2.08 g/cm3, ⫽ 0.9 cP, ⫽ 1 g/cm3, ⫽ 0.5, and S ⫽ 0.65 (for sand-type granular materials). The bed voidage is considered to be constant, which is true for low dp/D (see Section 3.6.3, Bed voidage considerations). From Figure 6.1, some important conclusions can be drawn. First of all, it is well known that on increasing the particle size, the adsorption and ion-exchange rates are decreased. Thus, the use of large particles should be avoided. On the other hand, beds with small particles should be operated at low velocities, especially in the upflow mode. In laboratory beds, this is not really a problem since in this way the requirement of high residence times is satisfied. Also, in these beds, upflow operation, which is essential to avoid liquid maldistribution and low liquid holdup, can be easily employed without exceeding the maximum linear velocity. However, in a large bed, keeping the same contact time and particle size as in the laboratory bed, the linear velocity would be increased and would probably exceed the maximum allowed linear velocity. Furthermore, upflow is generally avoided because in the downflow mode, the permissible maximum linear velocity is higher, leading to a safer operation.

3.5 upflow 3

downflow

us (cm/sec)

2.5 2 1.5 1 0.5 0 0

0.05

0.1 dp(cm)

0.15

0.2

Figure 6.1 The maximum linear velocity versus particle diameter for a fixed-bed operation (water as fluid phase).


537

Example 4 Consider a solid of 1 mm particle size. Assume that the adsorption will operate at a high contact time, for example, 0.2 h (5 BV/h relative flow rate). The maximum linear velocity for this particle size is approximately 0.5 cm/s for upflow and 1.5 cm/s for downflow operation. For a laboratory bed of 100 cm height and for the specified contact time, the linear velocity is about 0.13 cm/s. Then, it is safe to work under upflow conditions. However, for a large fixed bed of 1000 cm height and for the same contact time, the velocity would be about 1.3 cm/s. Consequently, it is not possible to work under upflow conditions, and downflow is highly recommended. It is obvious that the particle size should be chosen taking into account the kinetics and the hydraulics of both beds (laboratory and full scale). Apart from the velocity limitations of using small particle sizes, another problem could be the increased pressure drop, which in turn could result in the flooding of the bed in the downflow operation. Using this maximum working velocity and the contact time, the maximum bed height can be determined as follows: QFS ⫽ QrelVFS uFS ⫽

umax,FS QFS QrelVFS ⫽ ⱕ umax,FS Z FS ⱕ Qrel AFS AFS

(6.12)

where: Qrel ⫽ the number of bed volumes per hour, h⫺1 umax ⫽ the maximum allowable linear velocity, m/h A ⫽ the bed cross-sectional area, m2 At the bottom of the bed, the particles will be not only under the pressure of the other layers of solids but also under the hydraulic pressure of the liquid. Thus, the maximum bed height should be also be determined by using the particle strength data. The following equation could be used for the evaluation of the maximum bed height (Inglezakis, 2002): Zⱕ

Fc dp b gM p ( b + )

(6.13)

where: Z ⫽ the bed height b ⫽ the bed bulk density Mp ⫽ the particle weight Fc ⫽ the maximum force that can be applied to a single particle without breaking it (particle strength). For example, for clinoptilolite (common zeolite species with particle strength of about 2 Nt) and 1.4–1.7 mm particle size, the maximum bed high is very high, near 50 m. In any case, the lower height derived from the last two equations should be used.

538


Catalysis More or less, the comments above concerning the scale-up of the processes of adsorption and ion exchange in fixed beds can be also applied to catalysis. However, there are some points that should be emphasized in catalytic processes. To begin with, the great difference between the processes mentioned above and catalysis is that high temperatures, even as high as 1200 °C, are often employed in catalysis. So, temperature is a major variable in catalysis, beyond contact time and size of catalytic particles. It dominates the chemical reactions due to its exponential effect (Arrhenius), whereas a specific minimum value of temperature is required for the activation of catalysts. For example, automotive catalysts operate at temperatures above 250 °C. Therefore, the effect of temperature over a wide range has to be thoroughly examined in the laboratory and is essential for the sound evaluation of the reaction model and the accurate estimation of the activation energies. We should also keep in mind that temperature has a dramatic impact on catalytic reactor yield, catalyst selectivity, and product quality. Small variations of temperature can lead to undesirable effects. Consequently, we should also try to determine the response of other critical factors to temperature variation. The knowledge of the temperature effect is also critical for the design and control of reactors, otherwise temperature runaways may take place. Another issue that needs attention is that in large-scale beds, phenomena absent in the laboratory reactor may develop. For example, in commercial beds, axial gradients of temperature may appear, which are absent in bench beds due to the small diameters usually used in them. In the worst scenario, the controlling mechanism, and thus the whole behavior of the system, could change from the small to the large scale. The approach followed for scaling-up may be also characterized by inherent weaknesses. Specifically, the application of physical modeling, either using the similarity analysis or by means of dimensionless groups, should be carried out very carefully, and even then it should be properly checked before applied. For example, a similarity analysis may lead to the conclusion that the effectiveness factor of the catalyst in the large unit should be much lower than the one in the bench scale. However, that would be really unproductive from the practical point of view, since the high effectiveness of the catalyst is generally highly desirable. Moreover, geometrical similarity may order the use of larger particles in the large unit, which could result in various problems. First of all, the use of particles as large as the similarity ratio dictates could be from unpractical to unfeasible, since we cannot use huge catalytic particles. Moreover, in laboratory reactors, very small sizes of particles are generally used and as a result the effectiveness factors are very close to 1. On the other hand, the use of larger particles in commercial units could adversely affect the catalyst effectiveness factors, let alone the appearance of intraparticle heat and mass transfer phenomena. Furthermore, we should keep in mind that by varying the size of catalytic particles, we also change what is really important in catalytic systems: the available catalytic surface (internal and external) to the reactants per reactor volume. Haakana et al. (2004) used various mock-up experiments to achieve deep understanding of the different simultaneous phenomena in monolith reactors. However, they pointed out that the results obtained from mock-up experiments might not properly represent the overall reactor performance. This is an inherent limitation of mock-up experiments, where each phenomenon is studied in the absence of the others. However, during the “real” operation


539

of the reactors, all phenomena take place simultaneously, interacting one another. These interactions cannot be revealed in mock-up experiments. For example, in the specific case, the authors estimated gas–liquid mass transfer parameters in conditions of absence of chemical reaction. However, the overall mass transfer rate could be even higher in the presence of reaction. Moreover, the use of model fluids in mock-up experiments instead of real ones was also a source of inaccuracies. They concluded that the most feasible approach was to combine the detailed knowledge from different mock-up experiments with information from pilot tests including all the relevant phenomena. Donati and Paludetto (1999) have presented the problems faced during the start-up of the emulsion polymerization of tetrafluoroethylene in a semibatch reactor, regarding monomer purity, initiator addition policy, type and quantity of surfactants, and the temperature and pressure to be operated. No answer could be found for one problem: the failure to reproduce in the pilot plant the productivity and emulsion concentration obtained in the laboratory. Although sophisticated mathematical models had been developed including various phenomena such as initiation, radical propagation and polymer chain termination balances, molecular weight distribution, particle growth, and population balances, the loss in productivity was neither predicted nor expected. Simple experiments illuminated the cause of that deficiency: the effect of stirrer speed on productivity and product quality had not been taken into account. The autoclaves used had no internals and the stirrer had been selected on the basis of suppliers’ experience. The study was therefore focused on reactor fluid dynamics and on the selection of stirring devices. The use of various dimensionless groups for scaling-up can also lead to contradictory results. If two different values for a parameter are estimated by means of two dimensionless groups, then the value that satisfies both phenomena related to these groups should be selected. To sum up, special attention should be given to the effect of temperature on the process during the design and control of commercial catalytic reactors. Moreover, the same size of catalyst particles should be used at any scale so that the catalyst effectiveness factor also remains the same. The available catalytic surface per reactor volume and the space velocity (when the rate is not controlled by mass transfer phenomena) should also be left unchanged at any scale. 6.2.2 Trickle-bed reactors The scale-up of trickle beds presents many difficulties mainly due to maldistribution of fluids, which leads to different routes for the liquid and gas, stagnant zones, and hot spots. In trickle-bed reactors, the particle diameter and residence time are the same for all scales. The consequence is that in different scales we have different Reynolds numbers and velocities. It is highly desirable to run laboratory trickle-bed reactors at a well-defined catalyst wetting state, often complete wetting, while matching the same liquid space hour velocity (LHSV, in h⫺1) of the large units (Dudukovic et al., 1999). To achieve this, small reactors are operated under upflow conditions where complete catalyst wetting can be obtained at the expense of a much larger liquid holdup than in the large reactor (packed bubble bed reactor). This may be undesirable, if side reactions occur in the liquid phase or if gas–liquid mass transfer is impaired by larger liquid-film resistance in the small unit. To avoid these dissimilarities between the two scales, the voids in the small reactor are filled with inert fines

540


(Al-Dahhan and Dudukovic, 1996; Dudukovic et al., 1999). This way, more solid–liquid contact points over which the liquid flows are created and the bed porosity is reduced, especially near the reactor wall. Following a proper procedure for packing a trickle bed with catalyst particles and fines decouples the apparent kinetics from hydrodynamics, which is highly desirable. The addition of fines is not the same as reducing the particle size of the catalyst, as in the latter case the particle effectiveness factor is smaller. Failing to identify the limiting reactant can lead to failure in the scale-up of trickle-bed reactors (Dudukovic, 1999). Gas-limited reactions occur when the gaseous reactant is slightly soluble in the liquid and at moderate operating pressures. For liquid-limited reactions, concurrent upflow is preferred (packed bubble columns) as it provides for complete catalyst wetting and thus enhances the mass transfer from the liquid phase to the catalyst. On the other hand, for gas reactions, concurrent downflow operation (trickle-bed reactors), especially at partially wetted conditions, is preferred as it facilitates the mass transfer from the gas phase to the catalyst. The differences between upflow and downflow conditions disappear by the addition of fines (see Section 3.7.3, Wetting efficiency in trickle-bed reactors). To conclude, for liquid-limited reactions scale-up can be based on constant LHSV. Thus, the large bed superficial velocity is much higher and the wetting efficiency is increased, which results in a better performance of the large-scale reactor. However, for gas-limited reactions this rule cannot be followed, because it leads to the opposite result as regards the reactor performance. The solution is to keep the same LHSV and constant reactor height, which means that scale-up is based on the same liquid superficial velocity and bed height. This may lead to undesirable “pan-cake” reactor geometry, where the problem is bad liquid distribution.

6.2.3 Fluidized beds Processes that involve the reaction of gases and solids are extremely difficult to handle mainly due to solid flow difficulties (Knowlton, 2000). The difficulty in the scale-up of these reactors constitutes their main disadvantage. The maximum scale-up factor for fluidized beds is usually between 50 and 100, whereas for fixed beds it could reach the value of 10,000. This is due to the fact that the flow characteristics are very different in the small and the large reactor: the bubble diameter does not change upon scale-up, whereas reactor diameter does. In addition, changes in the flow rate of the substrate stream in turn cause complex alterations in the flow pattern within these reactors, which may lead to consequent unexpected effects upon the conversion rate. The most useful tool to solve such problems in fluidized beds is the employment of cold-flow models. Thus, it is not surprising that most work in fluidized beds has been focused on the cold model behavior and thus on their hydraulic behavior. Although the scale-up of fluidized beds has a history of over 50 years in the chemical industry, it is still more of an art than an exact science. Mathematics, history, and intuition are all involved in each new process brought to commercial practice (Matsen, 1996). The design factors of the scale-up of fluidized beds can be generally classified in two categories: •

Those that do not depend on the scale of the bed, such as the bed density and the gas velocity at minimum fluidization.


•

541

Factors that are affected by the size of the bed, mainly those that are connected to the bubble behavior. The size of the bubbles has an impact on various physical properties such as the bed density, whereas it also influences the gas–solids contact and reactor performance (Matsen, 1996). For example, in small equipment, the bubbles may have dimensions approaching those of the bed, whereas it is not the case in large beds and a scale-up effect will surely exist.

The operation of fluidized beds is connected to fluid mechanics within the beds (Nicastro and Glicksman, 1984). For example, heat and mass transfer are greatly influenced by the solid and fluid flow patterns, which are in turn affected by the bed geometry and internal configuration. Consequently, a thorough knowledge of fluid dynamics is a prerequisite to the successful design of a commercial unit. Most of the research on the dynamics of fluidized beds has been conducted in laboratory-scale beds, at low velocities and temperatures, with small particles (Nicastro and Glicksman, 1984). The extrapolation of these results to commercial beds is rather unreliable, particularly when severe conditions of temperature and pressure are employed. Moreover, as already shown, it is highly unlikely that the fluidization characteristics of the large bed will be the same as those in the laboratory unit. Computer simulations are also only as accurate as their input data and empirical correlations. It seems that only pilot or larger-scale plants can provide reliable information that can be extended to commercial units. However, they present high cost of construction and difficulties in handling (Nicastro and Glicksman, 1984). It is more efficient to conduct properly designed experiments in smaller beds operated at ambient conditions as long as there is a way to apply these results to larger-scale beds at actual operating conditions with accuracy. In many cases, commercial beds operate at elevated temperatures with fluid density and viscosity far from ambient conditions. Under these conditions, laboratory experiments are difficult to conduct and it is preferable to study such systems under ambient conditions, and then extrapolate the results to the actual operating conditions by using appropriate scaling relationships (Glicksman, 1984). This course of action is very attractive, as it exhibits many advantages (Yang et al., 1995): • • • • • •

easy construction and control material problems associated with severe operating conditions can be avoided plastic material that provides full visibility of the unit during operation can be used detailed measurements of bed behavior can be easily conducted less manpower low cost.

Scale-up methods By converting the governing hydrodynamic equations for a particular system into nondimensional ones, Horio et al. (1986) and Glicksman (1988) derived the so-called “scaling laws” for fluidized beds. These laws should be seen as a guide to design small-scale, coldflow models, which simulate the hydrodynamic behavior of the commercial units (Knowlton et al., 2005).

542


Gliksman’s approach The result of the conversion of equations into nondimensional ones is a set of dimensionless parameters (Froude number, velocity, particle size, diameter ratios, etc.) that should be matched in both small and large systems. It is not necessary for the values of the parameters to be equal in each system. Instead, the dimensionless number ratios have to remain the same. To achieve this, the particle size and/or the particle density of the solids have to be changed appropriately in the small unit. It usually results in a smaller particle size in the small unit compared to the large one. Working with the equations of motion and conservation of mass for both fluid and particles, we have the following nondimensional parameters (Glicksman, 1984): h dp3 g us2 Z D , , , , , S , particle size distribution, bed geometry gdp h dp dp 2 The first term is a modified Archimedes number, while the second one is the Froude number based on particle size. Alternatively, the first term can be substituted by the Reynolds number. To attain complete similar behavior between a hot bed and a model at ambient conditions, the value of each nondimensional parameter must be the same for the two beds. When all the independent nondimensional parameters are set, the dependent parameters of the bed are fixed. The dependent parameters include the fluid and particle velocities throughout the bed, pressure distribution, voidage distribution of the bed, and the bubble size and distribution (Glicksman, 1984). In the region of low Reynolds number, where viscous forces dominate over inertial forces, the ratio of gas-to-solid density does not need to be matched, except for beds operating near the slugging regime. Example 5 Consider a fluidized bed operated at an elevated temperature, e.g. 800°C, and under atmospheric pressure with air. The scale model is to be operated with air at ambient temperature and pressure. The fluid density and viscosity will be significantly different for these two conditions, e.g. the gas density of the cold bed is 3.5 times the density of the hot bed. In order to maintain a constant ratio of particle-to-fluid density, the density of the solid particles in the cold bed must be 3.5 times that in the hot bed. As long as the solid density is set, the Archimedes number and the Froude number are used to determine the particle diameter and the superficial velocity of the model, respectively. It is important to note at this point that the rule of similarity requires the two beds to be geometrically similar in construction with identical normalized size distributions and sphericity. It is easy to prove that the length scales (Z, D) of the ambient temperature model are much lower than those in the hot bed. Thus, an ambient bed of modest size can simulate a rather large hot bed under atmospheric pressure. However, this type of scaling should be avoided in the scale-up of new processes, where the knowledge of issues such as reaction effects and attrition is required. So, the results obtained from tests with different particle sizes and/or particle density cannot be directly applicable to the process (Knowlton et al., 2005). Interparticle forces appearing in fluidized beds cannot be taken into account using scaling laws, because it is almost impossible to characterize these forces. Moreover, it is usual


543

to employ small particle sizes in small units, which may result in different types of solids used at the small and the large scale. For example, Group B solids may be used in the large unit, whereas the small bed will operate with Group A solids. It is obvious that the change of the particle size, and therefore of the type of solids, will in turn alter the fluidization behavior in the reactor. To sum up, our effort to achieve dimensional similarity between the small and the large unit may lead to great alterations in particle size and thus different fluidization characteristics in each system (Knowlton et al., 2005). The same problems may also arise from scaling due to changes in the operating velocity. The latter may cause the existence of different flow regimes in the small and the large unit. So, though the dimensionless groups may be the same, the flow regime may vary significantly between the two scales, being in one bed in the bubbling region and in the other in the turbulent one. It is clear that the flow regime and the particle Geldart Group should be the same in both sizes of beds, if a successful scale-up is to be performed. From the above, we can now note that scaling should be applied to modifications of an existing unit rather than to a new one. Assume that a fluidized bed operates with a set of internals that need to be changed. One way to test the new set of internals would be to use the scaling laws to create a small, cold model, which would operate the same as the large, hot unit as regards the hydrodynamics. As long as the fluidizing regime does not change, due to the modification of the particle size in our effort to keep the dimensionless groups constant, this would be a fine application of the scaling laws (Knowlton, 2000; Knowlton et al., 2005). Horio’s approach Horio et al. (1986) suggested a similarity rule, which is valid in the viscous limit. The rule states that the hydrodynamic similarity between a base model and a reactor model that is m times larger is obtained when (a)

The geometrical and topological attributes of the bed, such as the bed diameter D, height Zf, distributor orifice diameter Pn and orifice pitch dn, are changed in the same proportion, that is m⫽

DL Z f,L dbub,L Pn,L dn,L ⫽ ⫽ ⫽ ⫽ DS Z f,S dbub,S Pn,S dn,S

(6.14)

where the subscript “L” refers to the large scale unit and “S” to the small scale unit. (b)

The minimum fluidization velocity ratio and the gas superficial velocity ratio in the two scales are equal to the square root of the scale ratio m: m⫽

ufm,L ufm,S

⫽

(us ⫺ ufm )L (us ⫺ ufm )S

(6.15)

Using these rules and basic hydrodynamic relationships for Ar, ufm, D, ubub, Zf, and dbub that can be found in Section 3.8.2, Kelkar and Ng (2002) derived the following relationships: s,L bub,L ⫽ ⫽1 s,S bub,S

(6.16)

544


us,L us,S

ubub,L

⫽

ubub,S

Ms,L

Qg,S

(6.17)

⫽ m3

(6.18)

⫽ m5 Ⲑ 2

(6.19)

Ms,S Qg,L

⫽ m

According to Kelkar and Ng (2002), these rules for hydrodynamic similarity have been validated by several studies and thus they are quite reliable. A further step is made by considering the mass transfer coefficient Lbe using the following equation: Lbe ⫽ 4.5

Dg0.5 g 0.25 ufm bub ⫹ 5.25 bub 5Ⲑ 4 dbub dbub

(6.20)

In this equation, the first term represents the convection term and the second the gas diffusion contribution to the mass transfer. Horio et al. (1986) defined the ratio of the diffusion term to the convection term (eq. (6.20)): ⫽

D 0.5 g 0.25 0.25 ufm dbub

(6.21)

For ⬍ 0.1, mass transfer is dominated by convection, whereas for ⬎ 10, diffusion dominates. Furthermore, using the above similarity rules, it is found that L ⫽ m⫺1Ⲑ 4 S

(6.22)

This proportionality means that the relative importance of diffusion in mass transfer decreases as the reactor size increases. Finally, the particle diameter should be adjusted using ufm in the large column:

Refm,L ⫽

(ufm,S m )dp,L ArL ⫽

⫽ (1136 ⫹ 0.0408 ArL )0.5 ⫹ 33.7

3 dp,L ( h ⫺ )g

2

(6.23)

(6.24)

It should be noted that this change in the particle diameter could lead to different diffusion characteristics and alter the effectiveness factor of the catalyst.


545

After the investigation of hydrodynamics and mass transfer, the next step is the examination of the reactor model. For example, let us consider here the two-phase model with plug flow of gas in both bubble and emulsion phase and first-order reaction (see Section 3.8.3). The first step at this stage is to transform its equations to dimensionless forms. For simplifying the calculations, we consider that the gas flows only through the bubble phase (fb ⫽ 1) and that there are no solids in the bubble phase ( b ⫽ 0). Under these conditions, the model of the reactor is (eqs. (3.519) and (3.520)) ⫺us

d Cb ⫺ Lbe (Cb ⫺ Cp ) ⫽ 0 dz

Lbe (Cb ⫺ Cp ) ⫺ (1⫺ bub )(1⫺ fm )kCp ⫽ 0

(6.25)

(6.26)

The dimensionless equations become ⫺

Cp,o   dG E ⫽0 ⫺ Nm  G ⫺ Cb,o  dZ 

  Cb,o N G ⫺ E  ⫺ (1⫺ bub )(1⫺ fm ) r E ⫽ 0 Nm  C s   p,o

(6.27)

(6.28)

The dimensionless parameters are given in eqs (6.31) to (6.35). Solving eq. (6.28) for E, E⫽

N m G Cb,o N m ⫹ N r Cp,o

(6.29)

Substituting into eq. (6.27), ⫺

dG  ⫺N m N r  G⫽0 ⫹ dZ  N m ⫹ N r 

(6.30)

where Z⫽

z Zf

(6.31)

G⫽

Cb Cb,i

(6.32)

546


Cp

E⫽

Lbe Z f us

(6.34)

kvs s Z f us

(6.35)

Nm ⫽

Nr ⫽

(6.33)

Cp,i

If all the dimensionless parameters in a reaction model are kept constant with scale change, a similarity in the reactor performance is expected, provided that the basic assumptions of the model remain unchanged in both scales, e.g. in our example the plug flow condition of gas in the bubble phase. Scale-up methodology following hydrodynamic similarity: Following hydrodynamic similarity (eqs. (6.14)–(6.22)) and applying its equations to our system, the following additional similarity rules are derived: N r,L N r,S

m

⫽

⫽ m

(6.36)

ufm bub dbub

(6.37)

m

For ⬍ 0.1, kbub ⬵ 4.5 Then N m,L N m,S

⫽1 (6.38)

For ⬎ 10, kbub ⬵ 5.25

Dg0.5 g 0.25 5Ⲑ 4 dbub

bub

(6.39)

Then N m,L N m,S

⫽ m⫺3 Ⲑ 4

(6.40)

Scale-up methodology following the dimensionless reactor model: Instead of using the dimensionless numbers of the model or the hydrodynamic rules, we can use the reactor model or its performance index.


547

Reactor model (eq. (6.30)): ⫺

dG  ⫺N m N r  G⫽0 ⫹ dZ  N m ⫹ N r 

(6.41)

Performance index (conversion–solution of eq. (6.30)):  N N  x ⫽ 1⫺ exp ⫺ m r   Nm ⫹ Nr 

(6.42)

For achieving similarity during scaling, the following should hold:   Nm Nr  Nm Nr  ⫺ N ⫹ N  ⫽ ⫺ N ⫹ N  r S m r L m

(6.43)

where the subscripts L and S denote large and small unit parameters, respectively. If we follow the similarity rule for Nr, we have to formulate a similarity rule for Nm in order to achieve the target set in eq. (6.43). After some calculations, we have

⫽

N m,L N m,S

⫽

m m ⫹ ( N m ⲐN r )S ( m ⫺1)

(6.44)

This is the required variation in Nm to have the same conversion in both scales. It is obvious that following hydrodynamic similarity eq. (6.40), the mass transfer mode is different between the two scales due to the change in the respective dimensionless parameters, and as a result the conversion will be also different: For ⬍ 0.1,  N m,S mN r,S  x ⫽ 1⫺ exp ⫺   N m,S ⫹ mN r,S 

(6.45)

 m⫺3 Ⲑ 4 N m,S m N r,S  x ⫽ 1⫺ exp ⫺ ⫺3 Ⲑ 4  ⫹ m N r,S   m

(6.46)

For ⬎ 10,

where the conversion in the small reactor will be higher:  N m,S N r,S  x ⫽ 1⫺ exp ⫺   N m,S ⫹ N r,S 

(6.47)

548


Thus, it is clear that hydrodynamic similarity does not lead to actual similarity between the two scales concerning the conversion. So, the large reactor should be designed following some hydraulic similarity rules, for example, the rule for Nr, and then similarity rules derived from the reactor model. In our example, Nm in the large reactor should follow rule (6.44), and therefore Nm,L should be adjusted controlling the value of dbub (Kelkar and Ng, 2002). This is possible by employing internals, baffles, or different distributor design.

6.2.4 Slurry bubble bed column reactors It has been shown that the two-phase model for bubbling gas–solid fluidized beds can be extended to slurry bubble column reactors operating in the heterogeneous flow regime (Krishna et al., 1998). Two phases are identified: the large fast-rising bubbles (dilute phase) and the slurry phase in which small bubbles are finely dispersed (dense phase). The dilute phase is equivalent to the bubble phase and the dense phase to the emulsion phase in the fluidized beds. According to this analysis, the scale effects for fluidized beds and slurry columns are analogous. 6.2.5 Agitated vessels Scale-up involves selecting mixing variables to achieve the desired performance in both pilot and full scale. This is often a difficult task, sometimes even impossible, using geometric similarity, so that the use of nongeometric impellers in the pilot plant compared to the impellers used in the industrial plant often allows closer modeling of the mixing requirements to be achieved (Perry and Green, 1999). For mixing operations, the scale-up criterion is customarily given as an overall equation (Montane et al., 2003): NDan ⫽ const.

(6.48)

where N is the rotational speed and Da the diameter of the impeller. The exponent (n) depends on the specific characteristics of the process. In agitated slurry reactors, this type of scale-up criterion is often derived from studies concerning the minimum rotational speed for the complete suspension of solids. The same type of scale-up criterion is derived by studying the solids distribution quality. Buurnam et al. (1986) described the quality of the solids distribution in terms of the height of the homogeneous zone above the suspension. The values of n derived from various studies on the solids distribution quality are within 0.67 (single pitched-blade turbine) and 0.93 (multiple pitched-blade turbines and multiple Rushton turbines) (Montane et al., 2003). In the design of liquid mixing systems, the following dimensionless groups are of importance (Holland, 1962): •

the Reynolds number, NDa2/, which represents the ratio of applied to viscous drag forces,


• •

549

the Froude number, N 2Da/g, which represents the ratio of applied to gravitational forces, the Weber number, N 2Da2/, which represents the ratio of applied to surface tension forces.

Here, Da is the impeller diameter and N the revolutions per unit time. The Reynolds, Froude, and Weber numbers are proportional to NDa2, N 2Da, and N 2Da2, respectively. It is obvious that if one of these groups is used for scale-up, the scale-up rules represented by the other groups are automatically violated, provided that the physical properties of the fluid remain the same. The groups could, of course, be made compatible by using fluids having different physical properties on the large and small scales. In liquid systems, we can suppress the effect of the Froude number by using baffles to eliminate vortexing and hence the gravitational effects. The Weber number is of some importance only when separate physical phases are present in the liquid mixing system. The general equation for fluid motion in a mixing system contains no less than 13 terms. Of these terms, nine define geometric boundary conditions. If these can be fixed, and strict geometric similarity be adhered to, the equation can be simplified and written as N p ⫽ CRe m Fr n

(6.49)

where NP is the power number. When the effect of the Froude number is negligible, the power number is a function of only Re and the shape factor C. The great majority of mixing operations can be handled by this equation. It should be stressed out that a power number versus Reynolds number curve for a particular system holds true irrespective of the size of the vessel. The curve depends only on the geometrical configuration. This is the reason why experiments on very small models can be used to determine the power characteristics of large-scale plant mixing systems. Generally, the suspension of solids in large-scale vessels is maintained by lower stirrer speeds compared to small vessels, if the geometrical similarity is applied. Large differences may also appear in connection with the power requirement between the small and the large scale, because the power per unit volume Ps varies with N 3Da2 according to Zwietering’s equation (McCabe et al., 1983). If satisfactory solids suspension is obtained in a small tank, whether judged by visual observations, particle velocities, or mass transfer rates, the safe scale-up rule is to keep geometrical similarity and constant power per unit volume. The ratios Da/DT ⫽ 1/3 and Za/DT = 0.25 are often recommended, though some prefer Da/DT ⫽ 0.4 for solids suspension. The critical speed may be reduced by decreasing the clearance, but it may be hard to start the stirrer if it is in a layer of solids very close to the bottom (McCabe et al., 1983). Reynolds numbers in large tanks will be higher, typically of the order of 5 to 25 times higher than those in small tanks. For a specific power input, much attention has to be given to the ratio of the impeller diameter-to-vessel during scale-up. This ratio depends to a great extent on the nature of agitation, as its optimum value is • • •

0.25 for dispersing a gas into a liquid 0.40 for the contact of two immiscible fluids 0.6 or more for some blending operations.

550


Keeping the power input stable in a specific operation, the impeller speed is inversely proportional to the impeller size. This means that the smaller the impeller, the higher the impeller speed. In general, operations that depend on large velocity gradients rather than on high circulation rates are best accomplished by small, high-speed impellers, whereas large, slow-moving impellers should be preferably used for operations that depend on high circulation rates (McCabe et al., 1983). Although the scale-up of agitated vessels is mainly based on geometrical similarity, there are various cases where this target is difficult to achieve. Furthermore, the application of geometrical similarity does not ensure the similarity of dynamic and kinematic phenomena. Experience and intuition have to be employed in the scale-up procedure (McCabe et al., 1983).

Appendix I Physical Properties of Water, Air and Selected Compounds

I.1 BULK-PHASE PROPERTIES OF SOLVENTS AND CARRIER GASES I.1.1 Water Water is the most common solvent of environmental importance and therefore its properties are presented in the following paragraphs. The dynamic viscosity of water can be calculated using the following equation (Reid et al., 1988):

ln( W ) ⫽⫺24.71⫹

4209 ⫹ 4.527 ⫻10⫺2 T ⫺ 3.376 ⫻10⫺5 T 2 T

(I.1)

The same equation can be safely used for dilute aqueous solutions. Another useful relationship is the following (Reid et al., 1988):

W (T2 )⫺0.2661 ⫽ W (T1 )⫺0.2661 ⫹

T2 ⫺ T1 233

(I.2)

where temperature is in K and viscosity in cP. The density of water can be evaluated using the following equation: T  2Ⲑ7  ⫺ 1⫺  647.3 

W ⫽ 0.3471⫻ 0.274

(I.3)

where temperature is in K and density in g/cm3. The same equation can be also used for dilute aqueous solutions. 551

552

Appendix I

In relation with the surface tension of water, the following correlation is available (IAPWS, 1994):

W ⫽ 235.8 1.256 (1⫺ 0.625)

(I.4)

⫽ 1⫺ TR

(I.5)

TR ⫽

T TC

(I.6)

where W is in mN/m = dyn/cm and TC is the critical temperature of the water (647.096 K). This equation is valid between the triple point 0.01 °C and the critical temperature, TC. The basic thermophysical properties of water in the region of 290⫺355 K (17⫺82 °C) are given in Table I.1. I.1.2 Gases Air is the most common carrier gas in environmental applications, whereas nitrogen and helium are frequently used in laboratory experiments. Furthermore, oxygen in oxidations and hydrogen in hydrogenations are often used as reactants. Consequently, the most important properties of these gases among others are presented hereinafter. Dynamic viscosity The dynamic viscosity of the selected gases at atmospheric pressure and temperature range 298⫺623 K are given in Table I.2. Generally, at low pressures, the correlation of Chung et al. is proposed (Reid et al., 1988; Chung et al., 1984): ⫽ 40.785

( MB ⫻ T )0.5 VC2 Ⲑ 3

(I.7)

Table I.1 Thermophysical properties of saturated water (Lide, 1991; IAPWS, 1994; Incropera and DeWitt, 1990) T (K)

(mN/m)

(10⫺6 Pa s)

(kg/m3)

cp (kJ/kg K)

f (10⫺3 W/m K)

290 300 315 325 335 345 355

73.7 71.7 69.2 67.5 65.8 64.1 62.3

959 855 631 528 453 389 343

0.999 0.997 0.991 0.987 0.982 0.977 0.971

4.184 4.179 4.179 4.182 4.186 4.191 4.199

598 613 634 645 656 668 671

I.1 Bulk-Phase Properties of Solvents and Carrier Gases

553

Table I.2 Dynamic viscosity for various gases at atmospheric pressure and temperature range 298⫺623 K (10⫺5 Pa s) Gas

298 K

373 K

473 K

523 K

573 K

623 K

Air Nitrogen Helium Oxygen Hydrogen

1.85 1.77 1.99 2.06 0.89

2.18 2.1 2.32 2.46 1.04

2.58 2.49 2.73 2.94 1.22

2.76 2.67 2.93 3.16 1.31

2.94 2.85 3.13 3.38 1.40

3.10 3.02 3.31 3.58 1.48

where the dynamic viscosity is in P, temperature in K, critical volume in cm3/mol and ⫺B

 T  ⫽ A F   TC 

   T   T  ⫹ C exp ⫺D  F   + E exp ⫺G  F      TC    TC  

⫽ 1⫺ 0.2756 ⫹ 0.05935mr4 ⫹

(I.8)

(I.9)

for 0.3 ⱕ F

T ⱕ 100 TC

(I.10)

where A ⫽ 1.16145, B ⫽ 0.14874, C ⫽ 0.52487, D ⫽ 0.77320, E ⫽ 2.16178, G ⫽ 2.43787, F ⫽ 1.2593, is the acentric factor, and mr the dimensionless dipole moment:

mr ⫽ 131.3

m (VCTC )0.5

(I.11)

where m is the dipole moment in debyes. For air, nitrogen, helium, oxygen, and hydrogen the dipole moment is zero. The parameter is a special correction factor for highly polar substances such as alcohols (0.1⫺0.2), acetic acid (0.09), and water (0.076). For other gases, such as sulfsulfur dioxide, it can be taken equal to zero. For nonpolar gases at high pressures, the correlation of Jossi et al. is preferably used for reduced density between 0.1 and 3 (Jossi et al., 1962; Reid et al., 1988; Perry and Green, 1999): ( ⫺ ⬚) B ⫻107 ⫹1  

1Ⲑ 4

⫽ 1.023 ⫹ 0.023364 r ⫹ 0.585332r ⫺0.407583r ⫹ 0.093324 4r

(I.12)

554

Appendix I

where viscosity is in Pa s, o is the viscosity at low pressure, and r the reduced density of the gas: r ⫽

V ⫽ C C MB

(I.13)

Here, the density is evaluated under the actual conditions. The critical molar volume of some important gases is given in Table I.3. Finally, the parameter B is given by B⫽

2173.42TC1Ⲑ 6

(I.14)

MB1Ⲑ 2 PC2 Ⲑ 3

The properties under critical conditions (PC, TC) for some important gases are given in Table I.4. Density The density of gases for temperatures above the critical temperature TC and pressure of few atmospheres can be evaluated by the ideal gas law (Perry and Green, 1999): ⫽ MB

P RT

(I.15)

Table I.3 Critical molar volume of various gases Gas

VC (m3/kmol)


0.0925 0.0892 0.0573 0.0734 0.0641 Table I.4

Critical pressure and temperature for several gases Gas

TC (K)

PC (107 Pa)


132.5 126.2 5.2 154.6 33.2

0.377 0.340 0.023 0.504 0.131


555

Practically, in environmental applications, the temperature ranges from 20 to 400 °C and pressure from 1 to 40 atm. According to available data (Perry and Green, 1999) for air, nitrogen, oxygen, and hydrogen, the compressibility factor is practically unity and only in severe conditions of pressure and temperature varies from 0.98 to 1.02. Thus, the ideal gas law can be safely used in most environmental applications. For pressures corresponding to a reduced pressure Pr up to 0.2 and nonpolar gases, Pitzer’s correlation can be used (Perry and Green, 1999): = MB

P Z f RT

(I.16)

where Zf is the compressibility factor and can be evaluated by using the following formula (Perry and Green, 1999):

Z f ⫽ 1⫹

Pr Tr

 B C D E  A⫺ T ⫺ T 2 ⫺ T 3 ⫺ T 8   r r r r 

(I.17)

where the reduced temperature and pressure are Tr ⫽

T TC

(I.18)

P PC

(I.19)

and Pr ⫽

where TC and PC are the critical temperature in K and critical pressure in Pa, respectively. The constants of the correlation are A ⫽ 0.1445 ⫹ 0.073

(I.20)

B ⫽ 0.330 ⫺ 0.46

(I.21)

C ⫽ 0.1385 ⫹ 0.50

(I.22)

D ⫽ 0.0121⫹ 0.097

(I.23)

where is the acentric factor of the gas. The properties needed for the estimation of gas density are given in Table I.5.

556

Appendix I

Basic thermophysical properties of selected gases The basic thermophysical properties of selected gases at 1 atm is shown in Table I.6. Gas constant units The value of the gas constant relies on the units used, as shown in Table I.7. Table I.5 Properties of various gases for the estimation of density Gas

MB

P (atm) for Pr ⫽ 0.2


28.95 28.01 4 32 2.02

0.00739 0.0377 ⫺0.39 0.0222 ⫺0.215

7.54 6.80 0.46 10.08 2.62

Table I.6 Basic thermophysical properties of selected gases at 1 atm (Incropera and DeWitt, 1990) Temperature (K) Air 300 350 400 450 500 550 600 650 700 750 800 Helium 300 350 400 450 500 550 600 650 700 750 800

(kg/m3)

cp (kJ/kg K)

f (10⫺3 W/m K)

1.1614 0.9950 0.8711 0.7740 0.6964 0.6329 0.5804 0.5356 0.4975 0.4643 0.4354

1.007 1.009 1.014 1.021 1.030 1.040 1.051 1.063 1.075 1.087 1.099

26.3 30.0 33.8 37.3 40.7 43.9 46.9 49.7 52.4 54.9 57.3

0.1625 ⫺ 0.1219 ⫺ 0.09754 ⫺ ⫺ ⫺ 0.06969 ⫺ ⫺

5.193 5.193 5.193 5.193 5.193 5.193 5.193 5.193 5.193 5.193 5.193

152 170 187 204 220 ⫺ 252 264 278 291 304 (Continued)


Table I.6 Temperature (K) Hydrogen 300 350 400 450 500 550 600 700 800 Nitrogen 300 350 000 450 500 550 600 700 800 Oxygen 300 350 400 450 500 550 600 700 800

557

(Continued)

(kg/m3)

cp (kJ/kg K)

f (10⫺3 W/m K)

0.8078 0.06924 0.06059 0.05386 0.04848 0.04407 0.04040 0.03463 0.03030

14.31 14.43 14.48 14.50 14.52 14.53 14.55 14.61 14.70

183 204 226 247 266 285 305 342 378

1.1233 0.9625 0.8425 0.7485 0.6739 0.6124 0.5615 0.4812 0.4211

1.041 1.042 1.045 1.050 1.056 1.065 1.075 1.098 1.220

25.9 29.3 32.7 35.8 38.9 41.7 44.6 49.9 54.8

1.284 1.100 0.9620 0.8554 0.7698 0.6998 0.6414 0.5498 0.4810

0.920 0.929 0.942 0.956 0.972 0.988 1.003 1.031 1.054

26.8 29.6 33.0 36.3 41.2 44.1 47.3 52.8 58.9

Table I.7 Units of gas constant Units

Value

L atm ᎏ mol K

0.082

m3 atm ᎏ kmol K

0.082

cal ᎏ mol K

1.987

J ᎏ mol K

8.314 (Continued)

558

Appendix I

Table I.7

(Continued)

Units

Value

m3Pa ᎏ mol K

8.314

cm3 atm ᎏ mol K

82.057

Btu ᎏ lbmol°R

1.9858

kWh ᎏ lbmol°R

5.98⫻10⫺4

ft-lbf ᎏ lbmol°R

1545.3

Note: °R; Degrees Rankine.

I.2 DIFFUSION COEFFICIENTS IN WATER AND GASES I.2.1 Diffusion coefficient of salts (electrolytes) in aqueous solutions For dilute solutions of a single salt completely dissociated, the diffusion coefficient is given by the Nernst⫺Haskell equation (Gambill, 1958; Reid et al., 1988)

Dw ⫽

 1 1 ⫹ RgT  n⫹ n⫺  F2  1 1  ⫹  ⫹ ⫺

(I.24)

where Dw is the liquid-phase diffusion coefficient at infinite dilution in cm2/s, i the limiting ionic conductances (ionic mobility) in (A/cm2)(V/cm)(greq/cm3), ni the valence of the ion, (⫹) for cation and (⫺) for anion (absolute values), Rg the ideal gas constant (8.314 J/mol K), T the absolute temperature in K, and F the Faraday constant (96,500 Cb/greq). For ionic mobility, the following equation could be used (Reid et al., 1988): i (T2 ) = i (T1 )

T2 334

(I.25)

where is the dynamic viscosity of the liquid in cP for temperature of T2 (K). Values of ionic mobility for various ions in water are shown in Table I.8. For the estimation of the diffusion coefficient of a single ion the second term in the right-hand part of the eq. (I.24) is replaced by i /n i .

I.2 Diffusion Coefficients in Water and Gases

559

Table I.8 Ionic mobility in water at 25 °C (Gambill, 1958; Reid et al., 1988) Ion

Mobility (A/cm2)(V/cm)(greq/cm3)

H⫹ Li⫹ Na⫹ K⫹ Cs⫹ NH4⫹ ½ Mg2⫹ ½ Mn2⫹ ½ Ca2⫹ ½ Sr2⫹ ½ Co2⫹ ½ Cu2⫹ ½ Hg2⫹ ½ Cd2⫹ ½ Pb2⫹ ½ Ni2⫹ ½ Fe2⫹ 1/3 Fe3⫹ 1/3 Cr3⫹ ½ SO42⫺ ½ C2O4 2⫺ ½ CO32⫺ OH⫺ NO3⫺ Br⫺ Cl⫺

349.8 38.7 50.1 73.5 73 (18 °C) 73.4 53.06 47.2 (18 °C) 59.5 59.46 46.2 (18 °C) 53.6 63.6 54 70 54 48.3 (18 °C) 65.5 (18 °C) 48.4 (18 °C) 80 67.6 (18 °C) 64.5 (18 °C) 199.18 71.44 72.7 (18 °C) 76.34

I.2.2 Diffusion coefficient of other solutes in aqueous solutions For the determination of diffusion coefficient of solutes (except from salts and ions) in water and dilute solutions (⬍10%) the Hayduk and Laudie equation is used (Lyman et al., 1990; Perry and Green, 1999): Dw ⫽

8.621⫻10⫺14 0.589 1.14 w VGB

(I.26)

where Dw is the diffusion coefficient in m2/s, w the viscosity of liquid in Pa s (cP, correlated for temperature), and VGB the molar volume of the gas at the normal boiling point in m3/kmol. The latter can be evaluated by the Tyn and Calus equation (Perry and Green, 1984, 1999) VGB ⫽ 0.285VC1.048

(I.27)

560

Appendix I

where VC is the molar volume of the gas under critical conditions (both volumes in cm3/mol). In Table I.9, the critical molar volumes of the some gases of great environmental interest are given. In the case that the diffusion coefficient is known at a given temperature (commonly at 20 °C) the following equation can be used (Pavlov et al., 1979): Dw (T ) ⫽ Dw (20 ⬚C)[1⫹ 0.02(T ⫺ 20)]

(I.28)

Table I.9 Critical molar volume of gases (Smith, 1981) Gas

VC (cm3/mol)a

Air Nitrogen monoxide Nitrous oxide Carbon dioxide Carbon monoxide Sulfur dioxide Hydrogen Hydrogen peroxide Nitrogen Oxygen Methane C6H6 CH3Cl CHCl3 CCl4

86.6 57 96.3 94 93.1 122 65 77.7 90.1 74.4 99.3 260 143 240 276

a

To convert into m3/kmol multiply values by 10⫺3.

Table I.10 Diffusion coefficient of inorganic gases in water at 20 °C (Pavlov et al., 1979) Gas

Dw (10⫺9 m2/s)a

Ammonia Nitrous oxide Carbon dioxide Sulfur dioxide Hydrogen sulfide Hydrogen Nitrogen Oxygen

1.8 1.8 1.8 1.7 1.6 5.3 1.9 2.1

a

To convert into cm2/s multiply values by 104.


561

Table I.11 Diffusion coefficient of organic gases in water at 25 °C Gas

Dw (10⫺6 cm2/s)a

Benzene Carbon tetrachloride Chlorobenzene 2-Chlorophenol DDD DDE DDT 1,2-Dichlorobenzene 1,4-Dichlorobenzene 3,3-Dichlorobenzidine 2,4-Dimethylphenol 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,6-Dinitrotoluene Ethylbenzene Hexachlorobenzene Hexachloroethane 2-Methylphenol Nitrobenzene Pentachlorophenol Phenol Toluene 1,2,4-Trichlorobenzene Trichloroethylene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol m-Xylene o-Xylene p-Xylene Vinyl chloride

9.8 8.8 8.7 9.46 4.76 5.87 4.95 7.9 7.9 6.74 8.69 9.06 7.06 7.26 7.80 5.91 6.8 8.3 8.6 6.1 9.1 8.6 8.23 9.1 7.03 6.25 7.8 10 8.44 13.4

a

To convert into m2/s multiply values by 10⫺4.

where temperature is in °C. The diffusion coefficients of some inorganic and organic gases of environmental importance in water are given in Tables I.10 and I.11, respectively. I.2.3 Diffusion coefficient of gases in binary gaseous mixtures The Fuller⫺Schettler⫺Giddings equation is proposed for the determination of the diffusion coefficient of gases in binary mixtures at low pressure (Lyman et al., 1990; Perry and Green, 1984, 1999): DBA ⫽ 0.01

T 1.75 M R0.5 P(VA1/3 ⫹VB1/3 )2

(I.29)

562

Appendix I

where DBA is the diffusion coefficient of a gas B in a gas A in m2/s, T the temperature in K, P the pressure in Pa, and VA and VB the diffusion volumes of gases in cm3/mol, and MR is MR ⫽

1 1 ⫹ MBA MBB

(I.30)

where MBA and MBB are the molecular weights of the gases. The diffusion volumes of molecules can be calculated by adding the volume increments of the individual atoms: Vi = ∑ ui

(I.31)

i

In Tables I.12 and I.13, the diffusion volumes of some molecules and the volume increments of atoms are given (Perry and Green, 1984, 1999).

Table I.12 Diffusion volumes of various molecules Gas

Vi (cm3/mol)

Hydrogen Helium Nitrogen Oxygen Air Carbon monoxide Carbon dioxide Nitrous oxide Ammonia Water Sulfur dioxide

7.07 2.88 17.9 16.6 20.1 18.9 26.9 35.9 14.9 12.7 41.1 Table I.13

Volume increments of atoms and structures Atom or structure

ui (cm3/mol)

Carbon Hydrogen Oxygen Nitrogen Chloride Sulfur Aromatic ring Heterocyclic ring

16.5 1.98 5.48 5.69 19.5 17 ⫺20.2 ⫺20.2


563

In the case that the diffusion coefficient is known at a given temperature (commonly at 20 °C) the following equation can be used (Pavlov et al., 1979):  T  Da (T ) ⫽ Da (25 ⬚C) P⫺1   298 

3Ⲑ2

(I.32)

where temperature is in K and the pressure in atm. The diffusion coefficients of some inorganic and organic gases of environmental importance in air under atmospheric pressure are presented in Tables I.14 and I.15, respectively. Table I.14 Diffusion coefficients of inorganic gases in air at 20 °C (Pavlov et al., 1979) Gas

Da (10⫺5 m2/s)a

Ammonia Hydrogen Sulfur trioxide Carbon dioxide Carbon disulfide Sulfur dioxide Nitrogen Oxygen

1.70 6.11 0.94 1.38 0.89 1.03 1.32 1.78

a

To convert into cm2/s multiply by 104.

Table I.15 Diffusion coefficient of organic gases in air at 25 °C Gas

Da (10⫺2 cm2/s)a

Benzene Carbon tetrachloride Chlorobenzene 2-Chlorophenol DDD DDE DDT 1,2-Dichlorobenzene 1,4-Dichlorobenzene 3,3-Dichlorobenzidine 2,4-Dimethylphenol 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,6-Dinitrotoluene Ethylbenzene

8.8 7.8 7.3 5.01 1.69 1.44 1.37 6.9 6.9 1.94 5.84 2.73 20.3 3.27 7.50 (Continued)

564

Appendix I

Table I.15

(Continued)

Gas

Da (10⫺2 cm2/s)a

Hexachlorobenzene Hexachloroethane 2-Methylphenol Nitrobenzene Pentachlorophenol Phenol Toluene 1,2,4-Trichlorobenzene Trichloroethylene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol m-Xylene o-Xylene p-Xylene

5.42 0.25 7.4 7.6 5.6 8.2 8.7 3 7.9 2.91 3.18 7 8.7 7.69

a

To convert into m2/s multiply by 10⫺4.

I.3 SOLUBILITY OF GASES IN LIQUIDS—HENRY’S CONSTANT I.3.1 General The solubility CL of a gas in a liquid can be calculated via Henry’s constant, which is defined through the following equations: CG ⫽ HCL

(I.33)

Ppartial ⫽ H o CL

(I.34)

or

Following these equations, the units of Henry’s constant are

H

(mol/cm 3 )gas (mol/cm 3 )liquid

 atm cm 3  or H o   mol 

Frequently, H is referred to as the dimensionless Henry’s constant. To convert from one unit into the other:

H⫽

Ho RT

(I.35)

I.3 Solubility of Gases in Liquids–Henry’s Constant

565

The value of R is 82.057 (atm cm3/mol K). Another common unit used is that of Hf (MPa mol/mol) :  atm  MB(g/mol) H o ⫽ H f 10   MPa  L (g/cm 3 )

(I.36)

where the liquid density should be estimated for the given temperature. Generally, attention should be given to the units of the Henry’s constant found in literature. I.3.2 Henry’s constant of gases in water The following correlation for the estimation of Henry’s constant is available (IAPWS, 2004):  Hf  A B0.355 ⫽ ⫹ ⫹ CTR⫺0.41exp() ln  TR  pV  TR

(I.37)

⫽ 1⫺ TR

(I.38)

TR ⫽

T TC

(I.39)

where Hf is Henry’s constant in (MPa mol)/mol, TC the critical temperature of the solvent (647.096 K for water), pV the vapor pressure of the solvent at the temperature of interest, and A, B, and C the characteristic constants of dissolved gases (Table I.17). The vapor pressure can be calculated from the following equation (IAPWS, 2004): p  1 ln  V  ⫽  PC  TR

n

∑ ai bi

(I.40)

i⫽1

where PC is the critical pressure of the solvent (22.064 MPa for water) and n, ai, and bi are characteristic constants of the solvent (n ⫽ 6 for water, see Table I.16). Using the tabulation of Perry and Green (1999), the following equation can be obtained for Henry’s constant for air in the temperature range 5⫺90 °C and water as solvent: H f ⫽ 103 (⫺0.0008T 2 ⫹ 0.1474T ⫹ 4.0738)

(I.41)

where T is the temperature in °C. Henry’s constant is slightly dependent on the partial pressure of the gas. For example, for oxygen at 23 °C, Henry’s constant is increased by 8.7% for a 10 times increase in partial pressure (800⫺8200 mmHg) (Perry and Green, 1999).

566

Appendix I

Table I.16 Characteristic constants for water as solvent (IAPWS, 2004) ai

bi

n

⫺7.86 1.84 ⫺11.79 22.68 ⫺15.96 1.8

1 1.5 3 3.5 4 7.5

1 2 3 4 5 6

Table I.17 Characteristic constants for gases (IAPWS, 2004) Solute

A

B

C

Temperature range of validity (K)

H2 N2 O2 CO CO2 H2S CH4 C2H6 SF6

⫺4.73 ⫺9.68 ⫺9.45 ⫺10.53 ⫺8.55 ⫺4.52 ⫺10.45 ⫺19.68 ⫺16.56

6.09 4.72 4.44 5.13 4.01 5.24 4.67 4.51 2.15

6.06 11.71 11.42 12.01 9.52 4.42 12.13 20.63 20.35

273⫺636 278⫺636 274⫺616 278⫺588 274⫺642 273⫺533 275⫺633 275⫺473 283⫺505

Another correlation for estimating Henry’s constant for oxygen dissolved in water in the temperature range 100⫺200 °C has been presented by Wu et al. (2003): H f (T) ⫻10⫺3 ⫽ 761.1⫺108.9lnT ⫺ 40785.5 ⲐT

(I.42)

where T is in K. Henry’s constants for several atmospheric gases and organic compounds in water are shown in Tables I.18 and I.19, respectively. I.3.3 The case of oxygen A distinction should be made between solubility and Henry’s constant. Solubility is normally expressed in mg/L and is written as CL in the following relationship: CG ⫽ HCL

(I.43)

I.3 Solubility of Gases in Liquids–Henry’s Constant

567

Table I.18 Henry’s constants (10⫺3/Ho) for atmospheric gases with water as solvent at 25 °C Gas

Henry’s constant (mol/L atm) 1.3 ⫻ 10⫺3 1.9 ⫻ 10⫺3 4.8 ⫻ 10⫺3 1 ⫻ 10⫺2 2.5 ⫻ 10⫺2 3.4 ⫻ 10⫺2 0.12 1.1 1.23 6 49 62 220 3.6 ⫻ 103 8.8 ⫻ 103 2.1 ⫻ 105 2.1 ⫻ 105

O2 NO C2H4 NO2 N2O CO2 H2S HCl SO2 CH3O2 HNO2 NH3 CH3OH HCOOH CH3COOH HNO3 NO3

Note: The values given reflect only the physical solubility of the gas regardless the subsequent fate of the dissolved species, i.e. dissociation or other transformation in aqueous phase.

Table I.19 Dimensionless Henry’s constants for organic compounds and water as solvent at 25 °C (partly from Howe et al., 1986) Gas

H

Benzene Carbon tetrachloride Chlorobenzene Chloroform 2-Chlorophenol Ethylene dibromide DDD DDE DDT 1,2-Dichlorobenzene 1,4-Dichlorobenzene 3,3-Dichlorobenzidine 2,4-Dimethylphenol 2,4-Dinitrophenol 2,4-Dinitrotoluene 2,6-Dinitrotoluene Ethylbenzene Hexachlorobenzene

0.216 1.25 0.147 0.172 0.016 0.027 1.64 ⫻ 10⫺4 8.61 ⫻ 10⫺4 3.32 ⫻ 10⫺4 0.0642 0.130 1.64 ⫻ 10⫺7 8.20 ⫻ 10⫺5 1.82 ⫻ 10⫺5 3.8 ⫻ 10⫺6 3.06 ⫻ 10⫺5 0.323 0.0541 (Continued)

568

Appendix I

Table I.19

(Continued)

Gas

H

Hexachloroethane 2-Methylphenol Nitrobenzene Pentachlorophenol Phenol Toluene 1,2,4-Trichlorobenzene Trichloroethylene 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol m-Xylene p-Xylene o-Xylene

0.159 4.92 ⫻ 10⫺5 9.84 ⫻ 10⫺4 10⫺6 1.63 ⫻ 10⫺5 0.262 0.0582 0.422 1.78 ⫻ 10⫺4 3.19 ⫻ 10⫺4 0.304 0.304 0.199

8000

Hf (MPa*mol/mol)

7000 6000 5000 4000 3000 2000 1000 0

0

50

Figure I.1

100

150 200 T (°C)

250

300

350

Henry’s constant of oxygen in water.

It is evident that CL is a function of Henry’s constant and the concentration of the gas (or its partial pressure). It is clear from Figure I.1 that Henry’s constant increases till the temperature of 100 °C and then decreases. As a result, under constant gas-phase concentration (or partial pressure), the solubility of oxygen decreases up to 100 °C and then increases. Consequently, the common belief of continuous decrease in the solubility of gases in water by increasing temperature is true up to a point (Debellefontaine et al., 1996, 2000). There is a certain temperature, different for each gas species, above which this picture is reversed. Moreover, above a certain temperature, the volatilization of water decreases the partial pressure of the gas, and thus the solubility could be further decreased even if Henry’s constant is decreased at the same time.

I.4 Parameters for Dubinin–Raduskevish Isotherm

569

Note that the solubility of oxygen is frequently overestimated via Henry’s constant due to the fact that solubility refers to air as gas, and thus the partial pressure of oxygen is 0.21 atm while Henry’s constant is based on 1 atm gas partial pressure.

I.4 PARAMETERS FOR DUBININ–RADUSKEVISH ISOTHERM In this section, the most important parameters for the Dubinin⫺Raduskevish isotherm are presented. I.4.1 Saturated vapor pressure of selected organic compounds Vapor pressure is the most important of the basic thermodynamic properties affecting both liquids and vapors. The vapor pressure is the pressure exerted by a pure component at equilibrium at any temperature (Perry and Green, 1999). The proposed equation is the modified Riedel (Perry and Green, 1999): B ln Ps ⫽ A ⫹ ⫹ C ln T ⫹ DT E T

(I.44)

where the vapor pressure Ps is in Pa, the temperature T in K, and A, B, C, D, and E the characteristic constants of the compound. In Table I.20, the constants of some important organic compounds for environmental applications are given. I.4.2 Molar volumes in the liquid state of selected organic compounds In Table I.21, the molar volumes in the liquid state of some important organic compounds found in many environmental applications are given. Pavlov et al. (1979) directly gives the values of the affinity coefficient based on the molar volumes for selected compounds (Table I.22). Table I.20 Constants of organic compounds for the modified Riedel equation Compound

A

B

C

D

E

Toluene Benzene Nitrobenzene Methane Tetrachloroethylene Styrene Vinyl chloride Cyclohexane Phenol

80.88 83.22 90.445 38.664 58.764 105.93 91.43 116.51 95.44

⫺6902 ⫺6517.7 ⫺9744.8 ⫺1314.7 ⫺6191.2 ⫺8685.9 ⫺5141.7 ⫺7103.3 ⫺10113

⫺8.7761 ⫺9.3453 ⫺9.5228 ⫺3.373 ⫺5.3312 ⫺12.42 ⫺10.981 ⫺15.49 ⫺10.09

0.58 ⫻ 10⫺5 0.712 ⫻ 10⫺5 0.756 ⫻ 10⫺17 0.302 ⫻ 10⫺4 0.213 ⫻ 10⫺5 0.756 ⫻ 10⫺5 0.193 ⫻ 10⫺4 0.169 ⫻ 10⫺1 0.676 ⫻ 10⫺17

2 2 6 2 2 2 2 1 6

570

Appendix I

Table I.21 Molar volumes of organic compounds in the liquid state m3/kmol Compound

V (m3/kmol)

Toluene Benzene Nitrobenzene Methane Tetrachloroethylene Styrene Vinyl chloride Cyclohexane Phenol

0.107 0.089 0.103 0.038 0.103 0.130 0.065 0.109 0.089

Table I.22 Affinity coefficient based on the molar volumes for selected compounds Compound

V (m3/kmol)

Methyl alcohol Butane Propane Acetone Acetic acid Benzene Cyclohexane Diethylethere Toluene Hexane Heptane

0.4 0.9 0.78 0.88 0.97 1 1.03 1.09 1.25 1.35 1.59

I.5 CONSTANTS OF LANGMUIR AND FREUNDLICH ISOTHERMS The Langmuir and Freundlich constants for several systems are presented in Table I.23 and Table I.24, respectively. Table I.23 Langmuir adsorption isotherm constants for several compounds on several adsorbents/water systems at ambient temperature Compound

Adsorbenta

QM (mg/g)

Polyethylene glycol (PEG)

AC (F-400)

2022 0.014 Chang et al. (2004) (pH ⫽ 6.5) (pH ⫽ 6.5) 558 0.056 (pH ⫽ 0.25) (pH ⫽ 0.25)

K (L/mg)

Reference

(Continued)

I.5 Constants of Langmuir and Freundlich Isotherms

Table I.23

571

(Continued)

Compound

Adsorbenta

QM (mg/g)

K (L/mg)

Reference

Cinacron Rot F-B (C.I. 184—dye) Reactive blue 21 (C.I. 21—dye) 2-Naphthol

PAC

455

0.115

Voudrias et al. (2002)

PAC

357

0.275

AC

345

0.643

2-Chlorophenol

AC

272

0.405

Salicylaldehyde

AC

240

0.417

o-Cresol

AC

240

0.417

Benzaldehyde

AC

210

0.170

Benzyl alcohol

AC

158

0.176

Pb2⫹

Clinoptilolite (natural zeolite) AC

109⫺144 (pH⫽ 3⫺6) (pH⫽ 3⫺6) 0.05⫺0.09

Voudrias et al. (2002) Dobbs and Cohen (1980); Noble and Terry (2004) Dobbs and Cohen (1980); Noble and Terry (2004) Dobbs and Cohen (1980) Noble and Terry (2004) Dobbs and Cohen (1980); Noble and Terry (2004) Dobbs and Cohen (1980) Noble and Terry (2004) Dobbs and Cohen (1980); Noble and Terry (2004) Bektas and Kara (2004)

108

0.177

Fly ash

94.3

0.465

Dobbs and Cohen (1980); Noble and Terry (2004) Voudrias et al. (2002)

Plain and 87⫺114 acid-treated carbons Plain and 70⫺179 acid-treated carbons AC 55

0.114⫺0.287

⫺

0.014⫺0.044

⫺

0.154

Clinoptilolite 25.77 (natural zeolite) Activated 8.45 bleaching earth (clay) Mesoporous 7.09⫺8.55 aluminas (protonated and aminated) GAC 3.34⫺7.04 (Filtrasorb 200) AC 0.310

0.018

Dobbs and Cohen (1980); Noble and Terry (2004) Sarioglu (2005)

0.0552

Tsai et al. (2004)

0.25⫺0.31

Rengaraj et al. (2004)

2.05⫺2.55

Chen and Wang, (2004)

0.230

Dobbs and Cohen (1980); Noble and Terry (2004)

Aniline Reactive blue 21 (C.I. 21—dye) Aniline Phenol Pyridine NH4⫹ Acid orange 51 (acid dye) Cu2⫹

Cu2⫹ Nitrobenzene a

GAC, granular activated carbon; PAC, powdered activated carbon; AC, activated carbon (Filtrasorb 300).

572

Appendix I

Table I.24 Freundlich adsorption isotherm constants for several compounds on several adsorbents/water systems at ambient temperature Compound

Adsorbenta

KF (mg/g)(L/mg)Fr

Fr

Reference

PCB Endrin Aldrin Dieldrin Alachlor Hexachlorobenzene Pentachlorophenol 4-Nitrobiphenyl Styrene DDT 1,2-Dichlorobenzene m-Xylene DBS Ethylbenzene Cinacron Blau F-R (C.I. 182—dye) p-nitrophenol 2,4-Dichlorophenol 2,4,6-Trichlorophenol 2,4-Dinitrotoluene 2,4-Dinitrotoluene Naphthalene Maxilon Scharz FBL-01 300% (basic dye) 1,4-Dichlorobenzene Chlorobenzene Toluene 2-Nitrophenol Hexachloroethane 2,4-Dimethylphenol Maxilon Goldberg GL EC 400% (basic dye) 4-Nitrophenol Nitrobenzene 2-Chlorophenol Tetrachloroethylene Cinacron Blau F-R (C.I. 182—dye) 2,4-Dinitrophenol Trichloroethylene 1,2-Dibromoethene Phenol 1,2-Dichloropropane

AC AC AC AC AC AC AC AC AC AC AC AC AC AC PAC

14100 666 651 606 479 450 436 370 327 322 263 230 178 175 174

1.03 0.80 0.92 0.51 0.26 0.60 0.34 0.27 0.48 0.50 0.38 0.75 0.147 0.53 0.513

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Hashimoto et al. (1977) Dobbs and Cohen (1980) Voudrias et al. (2002)

AC AC AC AC AC AC GAC

172 157 155 146 145 132 124.4

0.113 0.15 0.40 0.31 0.32 0.42 0.219

Hashimoto et al. (1977) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Meshko et al. (2001)

AC AC AC AC AC AC GAC

121 100 100 99 97 78 77.82

0.47 0.35 0.45 0.34 0.38 0.44 0.123

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Meshko et al. (2001)

AC AC AC AC

76 68 51 51

0.25 0.43 0.41 0.56

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980)

Fly ash AC AC AC AC AC

41 33 28 22 21 19

0.274 0.61 0.62 0.46 0.54 0.59

Voudrias et al. (2002) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) (Continued)

I.5 Constants of Langmuir and Freundlich Isotherms Table I.24

573

(Continued)

Compound

Adsorbenta

KF (mg/g)(L/mg)Fr

Fr

Reference

Cinacron Rot F-B (C.I. 184—dye) Cinacron Blau F-R (C.I. 182—dye) Carbon tetrachloride 1,2-Trichloropropene Maxilon Scharz FBL-01 300% (basic dye) 1,1,2-Trichloroethane 1,1-Trichloroethylene 1,2-Trichloroethane Chloroform Maxilon Goldberg GL EC 400% (basic dye) 1,1,1-Trichloroethane 1,1-Dichloroethane Benzene Benzoic acid Chloroethane Cinacron Rot F-B (C.I. 184—dye)

Fly ash

18.7

0.237


Bentonite

15.4

0.390


AC 11 AC 8.2 Clinoptilolite 7.5 (natural zeolite) AC 5.8 AC 4.9 AC 3.6 AC 2.6 Clinoptilolite 2.58 (natural zeolite) AC 2.5 AC 1.8 AC 1.0 AC 0.76 AC 0.59 Bentonite 0.87

0.83 0.46 0.389

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Meshko et al. (2001)

0.60 0.54 0.83 0.73 0.284

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Meshko et al. (2001)

0.34 0.53 1.6 1.8 0.95 0.845

Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Dobbs and Cohen (1980) Voudrias et al. (2002)

a

GAC, granular activated carbon; PAC, powdered activated carbon; AC, activated carbon (Filtrasorb 300).

The values in Table I.24 should be seen only as a first guide or rough approximation since the parameters could be widely different with different carbons. For instance, in Table I.25, the Freundlich parameters are presented for benzene adsorption on several types of activated carbons. Table I.25 Freundlich constants for several carbons and benzene (USACE, 2001) Carbon

Kf (mg/g)(L/mg)Fr

Fr

Filtrasorb 300 Filtrasorb 400 Norit Nuchar WV-G Hydrodarco 1030

1 16.6 49.3 29.5 14.2

1.6 0.4 0.6 0.4 0.4

Appendix II Conversion of Units In Tables II.1⫺II.20, several basic units and the basic conversion factors (CF) are presented. Table II.1 Decimal multiples and submultiples Multiple

Submultiple

Prefix

Symbol

Operator

Prefix

Symbol

Operator

deca hecto kilo mega giga tera

da h k M G T

⫻ 10 ⫻ 102 ⫻ 103 ⫻ 106 ⫻ 109 ⫻ 1012

deci centi milli micro nano pico

d c m n p

⫻ 10⫺1 ⫻ 10⫺2 ⫻ 10⫺3 ⫻ 10⫺6 ⫻ 10⫺9 ⫻ 10⫺12

Note: For example, MJ ⫽ 106 J, kg ⫽ 103 g, dm ⫽ 10⫺1 m, pF ⫽ 10⫺12 F.

Table II.2 Basic physical constants Name

Symbol

Value

Units

Boltzmann constant Avogadro number Planck constant Faraday constant Gravity acceleration

k NA h F go

1.380658⫻ 10⫺23 6.02252 ⫻ 1023 6.626075 ⫻ 10⫺34 9.648530 ⫻ 104 9.80665

J/K 1/mol J s Cb/mol m/s2

Table II.3 Definitions of basic units Unit

SI

Equivalent

Force Energy Surface tension Dynamic viscosity Power Pressure

N J N/m Pa s W Pa

kg m/s2 m2 kg/s2 kg/s2 kg/m s m2 kg/s3 kg/m s2

575

576

Appendix II

Table II.4 Temperature units K ⫽ °C ⫹ 273.15 5 K ⫽ ᎏ ⫻R 9 5 °C ⫽ ᎏ (F ⫺ 32) 9 F ⫽ R ⫺ 459.67 9 F ⫽ ᎏ °C ⫹ 32 5

Table II.5 Length units ⫻ CF ⫽

m

in

ft

Mile

m in ft Mile

1 2.540 ⫻ 10⫺2 3.048 ⫻ 10⫺1 1.609 ⫻ 103

3.937 ⫻ 10 1 1.200 ⫻ 10 6.336 ⫻ 104

3.281 8.333 ⫻ 10⫺2 1 5.280 ⫻ 103

6.214 ⫻ 10⫺4 1.578 ⫻ 10⫺5 1.894 ⫻ 10⫺4 1

Note: Å ⫽ 10⫺10 m.

Table II.6 Mass units ⫻ CF ⫽

kg

lbm

slug

oz

Metric ton

kg lbm slug oz Metric ton

1 4.536 ⫻ 10⫺1 1.459 ⫻ 101 2.835 ⫻ 10⫺2 1.000 ⫻ 10⫺3

2.205 1 3.217 ⫻ 10 6.250 ⫻ 10⫺2 2.205 ⫻ 103

6.852 10⫺2 3.108 ⫻ 10⫺2 1 1.943 ⫻ 10⫺3 6.852 ⫻ 101

3.527 ⫻ 101 1.600 ⫻ 101 5.148 ⫻ 102 1 3.527 ⫻ 104

1.000 ⫻ 10⫺3 4.536 ⫻ 10⫺4 1.459 ⫻ 10⫺2 2.835 ⫻ 10⫺5 1

lbm/ft3

lbm/US gal

6.243 ⫻ 10 6.243 ⫻ 10⫺2 6.243 ⫻ 10⫺5 1 7.480

8.345 8.345 ⫻ 10⫺3 8.345 ⫻ 10⫺6 1.337 ⫻ 10⫺1 1

Table II.7 Density units ⫻ CF ⫽ 3

g/cm kg/m3 g/m3 lbm/ft3 lbm/US gal

g/cm3

kg/m3

g/m3

1 1.000 ⫻ 10⫺3 1.000 ⫻ 10⫺6 1.602 ⫻ 10⫺2 1.198 ⫻ 10⫺1

1.000 ⫻ 10 1 1.000 ⫻ 10⫺3 1.602 ⫻ 10 1.198 ⫻ 102 3

1.000 ⫻ 10 1.000 ⫻ 103 1 1.602 ⫻ 104 1.198 ⫻ 105 6

Conversion of Units

577

Table II.8 Volumetric feed units ⫻ CF ⫽ cm /s 3

3

cm /s m3/h L/mina ft3/s ft3/min gal/min a

1 2.778 ⫻ 102 1.667 ⫻ 10 2.832 ⫻ 104 4.719 ⫻ 10⫺2 6.309 ⫻ 10

3

m /h

ft3/s

L/min

3.600 ⫻ 10 1 6.000 ⫻ 10⫺2 1.019 ⫻ 102 1.699 2.271 ⫻ 10⫺1 ⫺3

6.000 ⫻ 10 1.667 ⫻ 10 1 1.699 ⫻ 103 2.832 ⫻ 10 3.785

⫺2

ft3/min

3.531 ⫻ 10 9.810 ⫻ 10⫺3 5.886 ⫻ 10⫺4 1 1.667 ⫻ 10⫺2 2.228 ⫻ 10⫺3 ⫺5

gal/min

2.119 ⫻ 10 5.886 ⫻ 10⫺1 3.531 ⫻ 10⫺2 6.000 ⫻ 10 1 1.337 ⫻ 10⫺1 ⫺3

1.585 ⫻ 10⫺2 4.403 2.642 ⫻ 10⫺1 4.488 ⫻ 102 7.480 1

L ⫽ dm3.

Table II.9 Surface tension units ⫻ CF ⫽ dyn/cm N/m kp/m pdl/ft lbf /ft

dyn/cm

N/m

kp/m

1 1.000 ⫻ 103 9.807 ⫻ 103 4.536 ⫻ 102 1.459 ⫻ 104

1.000 ⫻ 10 1 9.807 4.536 ⫻ 10⫺1 1.459 ⫻ 10 ⫺3

pdl/ft

1.020 ⫻ 10 1.020 ⫻ 10⫺1 1 4.625 ⫻ 10⫺2 1.488 ⫺4

lbf/ft

2.205 ⫻ 10 2.205 2.162 ⫻ 10 1 3.217 ⫻ 10

⫺3

6.852 ⫻ 10⫺5 6.852 ⫻ 10⫺2 6.720 ⫻ 10⫺1 3.108 ⫻ 10⫺2 1

Table II.10 Diffusion coefficient units ⫻ CF ⫽

cm2/s

m2/s

ft2/s

cm2/s m2/s ft2/s

1 1.000 ⫻ 104 9.290 ⫻ 102

1.000 ⫻ 10⫺4 1 9.290 ⫻ 10⫺2

1.076 ⫻ 10⫺3 1.076 ⫻ 10 1

Table II.11 Dynamic viscosity units ⫻ CF ⫽

Poise

Pa ⫻ s

kp ⫻ s/m2

lbm/(ft ⫻ s)

lbf ⫻ s/ft2

Poise Pa ⫻ s kp ⫻ s/m2 lbm/(ft ⫻ s) lbf ⫻ s/ft2

1 1.000 ⫻ 10 9.807 ⫻ 10 1.488 ⫻ 10 4.788 ⫻ 102

1.000 ⫻ 10⫺1 1 9.807 1.488 4.788 ⫻ 10

1.020 ⫻ 10⫺2 1.020 ⫻ 10⫺1 1 1.517 ⫻ 10⫺1 4.882

6.720 ⫻ 10⫺2 6.720 ⫻ 10⫺1 6.590 1 3.217 ⫻ 10

2.088 ⫻ 10⫺3 2.088 ⫻ 10⫺2 2.048 ⫻ 10⫺1 3.108 ⫻ 10⫺2 1

578

Appendix II

Table II.12 Kinematic viscosity units ⫻ CF ⫽

Stokes (cm2/s)

m2/s

ft2/s

Stokes m2/s ft2/s

1 1.000 ⫻ 104 9.290 ⫻ 102

1.000 ⫻ 10⫺4 1 9.290 ⫻ 10⫺2

1.076 ⫻ 10⫺3 1.076 ⫻ 10 1

Table II.13 Heat transfer coefficient units ⫻ CF ⫽

erg/(cm2⫻s⫻°C) W/(m2 ⫻ K)

⫻°C) erg/(cm2⫻s⫻ W/(m2 ⫻ K) ⫻°C) kcal/(m2⫻h⫻ ⫻pdl/(ft2⫻s⫻ ⫻F) ft⫻ ⫻F) Btu/(ft2⫻h⫻

1 1.000 ⫻ 103 1.163 ⫻ 103 8.165 ⫻ 102 5.678 ⫻ 103

1.000 ⫻ 10⫺3 1 1.163 8.165 ⫻ 10⫺1 5.678

kcal/(m2⫻h⫻°C) ft⫻pdl/(ft2⫻s⫻F) Btu/(ft2⫻h⫻F) 8.598 ⫻ 10⫺4 8.598 ⫻ 10⫺1 1 7.020 ⫻ 10⫺1 4.882

1.225 ⫻ 10⫺3 1.225 1.424 1 6.955

1.761 ⫻ 10⫺4 1.761 ⫻ 10⫺1 2.048 ⫻ 10⫺1 1.438 ⫻ 10⫺1 1

Table II.14 Force units ⫻ CF ⫽

dyn

N

kp

pdl

lbf

dyn N kP pdl lbf

1 105 9.807⫻105 1.382⫻104 4.448⫻105

10⫺5 1 9.807 1.382⫻10⫺1 4.448

1.020⫻10⫺6 1.020⫻10⫺1 1 1.410⫻10⫺2 4.536⫻10⫺1

7.233⫻10⫺5 7.233 7.093⫻10 1 3.217⫻10

2.248⫻10⫺6 2.248⫻10⫺1 2.205 3.108⫻10⫺2 1

Table II.15 Power units ⫻ CF ⫽ W erg/s ft ⫻ lbf/s Btu/h hp

ft ⫻ lbf/s

W

erg/s

1 1.000 ⫻ 10⫺7 1.356 2.931⫻10⫺1 7.457⫻102

1.000 ⫻ 10 1 1.356 ⫻ 107 2.931⫻106 7.457⫻109 7

7.376 ⫻ 10 7.376 ⫻ 10⫺8 1 2.162 ⫻ 10⫺1 5.5000 ⫻ 102 ⫺1

Btu/h

hp

3.412 3.412 ⫻ 10⫺7 4.626 1 2.544 ⫻ 103

1.341 ⫻ 10⫺3 1.341 ⫻ 10⫺10 1.818 ⫻ 10⫺3 3.930 ⫻ 10⫺4 1

Conversion of Units

579

Table II.16 Energy units ⫻ CF ⫽ erg

Joule

ft ⫻ lbf

kcal

kW ⫻ h

Btu

hp ⫻ h

erg Joule ft ⫻ lbf kcal kW ⫻ h Btu hp ⫻ h

10⫺7 1 1.356 4.187⫻103 3.600⫻106 1.055⫻103 2.684⫻106

7.376⫻10⫺8 7.376⫻10⫺1 1 3.088⫻103 2.655⫻106 7.782⫻102 1.980⫻106

2.388⫻10⫺11 2.388⫻10⫺4 3.238⫻10⫺4 1 8.598⫻102 2.520⫻10⫺1 6.412⫻102

2.778⫻10⫺14 2.778⫻10⫺7 3.766⫻10⫺7 1.163⫻10⫺3 1 2.931⫻10⫺4 7.457⫻10⫺1

9.478⫻10⫺11 9.478⫻10⫺4 1.285⫻10⫺3 3.968 3.412⫻103 1 2.545⫻103

3.725⫻10⫺14 3.725⫻10⫺7 5.050⫻10⫺7 1.560⫻10⫺3 1.341 3.929⫻10⫺4 1

1 107 1.356⫻107 4.187⫻1010 3.600⫻1013 1.055⫻1010 2.684⫻1013

Table II.17 Pressure units ⫻ CF ⫽ Pa Pa atm lbf/ft2 torr (mmHg) mm H2O psi (lb/in2) in Hg (0°C) in H2O (4°C)

atm

1 9.869 ⫻ 10⫺6 1.013 ⫻ 105 1 4.788 ⫻ 10 4.725 ⫻ 10⫺4 1.333 ⫻ 102 1.315 ⫻ 10⫺3 9.807 9.678 ⫻ 10⫺5 6.895 ⫻ 103 6.805 ⫻ 10⫺2 3.386 ⫻ 103 3.342 ⫻ 10⫺2 2.491 ⫻ 102 2.458 ⫻ 10⫺3

lbf/ft2

torr (mmHg) mm H2O

2.088 ⫻ 10⫺2 7.501 ⫻ 10⫺3 2.116 ⫻ 103 7.600 ⫻ 102 1 3.591 ⫻ 10⫺1 2.784 1 2.048 ⫻ 10⫺1 7.358 ⫻ 10⫺2 1.440 ⫻ 102 5.171 ⫻ 10 7.073 ⫻ 10 2.540 ⫻ 10 5.203 1.867

psi (lb/in2)

1.020 ⫻ 10⫺1 1.450 ⫻ 10⫺4 1.033 ⫻ 104 1.469 ⫻ 10 4.882 6.944 ⫻ 10⫺3 1.359 ⫻ 10 1.930 ⫻ 10⫺2 1 1.422 ⫻ 10⫺3 7.031 ⫻ 102 1 3.453 ⫻ 102 4.912 ⫻ 10⫺1 2.540 ⫻ 10 3.613 ⫻ 10⫺2

in Hg (0 °C)

in H2O (4 °C)

2.953 ⫻ 10⫺4 2.992 ⫻ 10 1.414 ⫻ 10⫺2 3.937 ⫻ 10⫺2 2.896 ⫻ 10⫺3 2.036 1 7.358 ⫻ 10⫺2

4.015 ⫻ 10⫺3 4.068 ⫻ 102 1.922 ⫻ 10⫺1 5.350 ⫻ 10⫺1 3.937 ⫻ 10⫺2 2.768 ⫻ 10 1.359 ⫻ 10 1

Table II.18 Surface units ⫻ CF ⫽

m2

ha

km2

yd2

acre

m2 ha km2 yd2 acre

1 1.000 ⫻ 104 1.000 ⫻ 106 8.361 ⫻ 10⫺1 4.047 ⫻ 103

1.000 ⫻ 10⫺4 1 1.000 ⫻ 102 8.361 ⫻ 10⫺5 4.047 ⫻ 10⫺1

1.000 ⫻ 10⫺6 1.000 ⫻ 10⫺2 1 8.361 ⫻ 10⫺7 4.047 ⫻ 10⫺3

1.196 1.196 ⫻ 104 1.196 ⫻ 106 1 4.840 ⫻ 103

2.471 ⫻ 10⫺4 2.471 2.471 ⫻ 102 2.066 ⫻ 10⫺4 1

Table II.19 Volume units ⫻ CF ⫽

m3

L

ft3

US gal

Barrel (oil)

m3 L ft3 US gal Barrel (oil)

1 1.000 ⫻ 10⫺3 2.832 ⫻ 10⫺2 3.785 ⫻ 10⫺3 1.590 ⫻ 10⫺1

1.000 ⫻ 103 1 2.832 ⫻ 101 3.783 1.59 ⫻ 102

3.5315 ⫻ 101 3.532 ⫻ 10⫺2 1 1.337 ⫻ 10⫺1 5.615

2.642 ⫻ 102 2.642 ⫻ 10⫺1 7.481 1 4.200 ⫻ 101

6.290 6.290 ⫻ 10⫺3 1.781 ⫻ 10⫺1 2.381 ⫻ 10⫺2 1

580

Appendix II

Table II.20 Solution concentration System name

Abbreviation

Definition

Common Units

Molarity Molality Normality Weight per volume Volume per volume Weight per weight Parts per million

M m N w/v v/v w/w ppm

Mole of solute per liter of solution Mole of solu te per kilogram of solvent Gram-equivalent of solute per liter of solution Grams of solute per volume of solvent Volume of solute per volume of solution Weight of solute per weight of solution Milligrams of solute per kilogram of solution

mol/L mol/kg greq/L g/cm3 cm3/cm3 g/g mg/kg

Note: The use of the ppm unit should not be made incautiously. It has to be clarified on first use whether ppm refers to mass per mass or to volume per volume. Concerning gases, mass-ppm (mg/kg) is different from volumeppm (cm3/m3). The estimation of density is required for connecting these units with each other. A simple choice is to express volume-ppm as ppmv. Furthermore, in aqueous solutions, ppm (mg/kg) is equivalent to mg/L due to the density of water (1 kg/L).

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Index carbon monoxide 2, 3, 5, 22, 24, 51, 503 incomplete combustion compounds 22 lead 2, 3, 26, 303 nitrogen dioxide 2, 3, 8, 11 nitrogen oxides and other nitrogen compounds 22, 24 ozone 1–6, 22, 23 particulate matter 2, 3, 5, 15, 23, 24, 27 photochemical oxidants 2 primary 2–4 secondary 2–4 sulfur dioxide 2, 3, 5, 6, 8, 11, 12, 14, 21, 394, 560, 562–563 sulfur oxides and other sulfur compounds 9, 21, 24 volatile organic compounds and organosilicon compounds 3, 22–24 air pollution Africa 11, 13, 15 Asia 11, 13, 14 Australia 11, 14, 15 European Union 5 global 2, 3, 5 Latin America 14 local 2, 3 North America 5, 12 regional 2–4 transboundary 3, 4 urban 2, 3 Akita–Yoshida correlation 119, 123, 129, 391 alumina 44, 47, 48, 243, 247, 251, 252, 356–357, 374, 421, 517, 520 Archimedes number 195, 200, 235, 237, 509, 542 axial mixing 149

A absorption 32, 38, 109 acentric factor 553, 555 Achwal–Stepanek correlation 179 acidification acid rain 2, 4, 11, 15, 21, 22, 25 dry deposition 4 activated carbon bituminous GAC 243, 244 granular 47, 163, 189, 243, 571, 573 lignite GAC 244 powdered 243, 347, 571, 573 active sites concentration 59, 60 adsorbent 31, 32, 43, 44, 46–48, 140, 231, 243–245, 247, 248, 250–252, 266, 267, 269, 289, 301, 327, 347, 348, 570–573 adsorption advantages 48 definition 31–33 disadvantages 48 environmental applications 28, 46–49 historical aspects 37–38 special application 49 affinity coefficient 269, 270, 569, 570 agitated slurry reactor (general) 78, 102, 114, 138, 548 slurry reactor modeling 107, 114, 131, 398 tank reactor 74, 167 ammonia 5, 6, 24, 37, 307, 315, 499 air emissions diffuse 21 ducted 21 fugitive 21 main source 6, 8, 23, 24 mobile sources 21 stationary sources 21 air pollutants 1,3-butadiene 2 benzene 2, 3, 23–25 carbon dioxide 5, 9, 21, 24

B baffled tanks 82, 95, 100, 101 Bayens equation 196, 201 bed voidage 93, 143, 147, 155, 157, 158, 180, 197, 199, 200 595

596

Index

bed-depth-service-time (BDST) model 195–197, 327, 329 benzene 2, 3, 23–25, 45, 104, 244, 246, 249, 270, 561, 563, 567, 569, 570, 573 best available techniques (BATs) 21, 28 bioaccumulation 18 biological oxygen demand 18, 26 Biot number 279, 290, 291, 301, 303, 305, 343 breakpoint concentration 308, 329–331, 346 breakthrough curve 308, 312, 316, 320, 339 bubble column hydraulic regimes 115 reactors 74, 78, 102, 104, 403 modeling 104, 386 bubble diameter agitated vessels 137 bubblbe column 123, 124, 126 fluidized bed 210, 212, 213, 540 bubbling bed model see fluidized bed models, Kunii–Levenspiel model bubbling fluidization 191, 193, 200, 203, 206, 207, 212, 213, 215, 226 Buckingham pi theorem 525 buoyancy force 234, 239 butadiene 2 C cadmium 20, 21, 26, 245, 260 Calderbank correlation 101, 129, 136–137, 139, 301 carbon dioxide 5, 9, 21, 24, 35, 49, 54, 348, 380, 560, 562, 563 carbon disulfide 21, 563 carbon monoxide 2, 3, 5, 22, 24, 50, 52–54, 250, 418, 499, 503, 560, 562 catalysis advantages 52 definition 34–37 disadvantages 52 environmental applications 28, 50–54 heterogeneous 35, 37, 41–44, 50 historical aspects 39–43 homogeneous 35, 36, 44, 45 special application 53 three-way 50, 52, 512 catalyst active sites 37, 57–59, 61, 65, 359–360 bulk density 63, 407

catalytic agents 231, 356 definition 34–37 molecular sieve 40, 48, 49, 248 monolithic 355, 407, 421, 422 porous 44, 60, 63, 64, 358, 373, 374, 382 promoters 356 specific area 59, 65 support 58, 355, 356, 461, 518 supported 65, 355, 515 surface arrangement 59, 65 three-way 50, 52, 512, 517 unsupported 356, 515 washcoat 355, 424, 515–517 catalyst deactivation attrition 517 catalyst inhibitor 512, 517 catalyst poison 512–513 Coulson and Richardson equation 512–513 fouling and coking 512–515 Froment and Bishoff approach 514 irreversible poisoning 517 kinetics of catalyst deactivation 518–520 leaching 518 management of spent catalysts 520–521 regeneration of deactivated catalysts 517–518 reversible poisoning 517 sintering 515–517 solid-state transformation 515–517 thermal aging 517–518 thermal degradation 512, 515–516 volatilization 516, 517 catalytic converter 6, 15, 43, 50, 52, 53, 421, 513, 517 catalytic oxidation 27, 28, 51, 367, 368, 401, 418, 465, 469, 473, 476, 491, 499, 518 catalytic reaction steps adsorption 43 desorption 44 dissociative adsorption 43 rate-limiting step 43 surface reaction 43 channeling 140, 158, 184, 192 chlorofluorocarbons 5, 6, 47 Chu–Kaiil–Wetterath correlation 224 Churchill correlation 161 Chung equation 150 Chung–Wen correlation 216 clays 37, 38, 45, 46, 248, 252, 253, 255 cold models 531

Index

597

compressibility factor 88, 555 constant pattern condition 311–312, 315, 316, 327, 332, 338 constriction factor 241 contact time 308, 309, 312, 316, 338, 339, 533–534, 536, 538 crush strength 231 D Damköhler number 371, 525 density aggregate 233, 234 apparent 232, 234 bulk 63, 232, 233, 407 effective 232 envelope 232 hydraulic 99, 121, 233–235, 240 particle 63, 121, 232, 233 skeletal 232, 233 solid 121, 233 true 232, 233 destructive methods for spent materials acid digestion 350 barix process 351 incineration 350 pyrolysis 349–350 vitrification 350 wet oxidation 350–351 diffusion coefficient solid phase 66, 86, 240–242, 265, 274, 280, 282, 285 liquid phase 101, 129, 165, 275, 291, 558–561 gas phase 226, 561–563 Knudsen diffusion 241, 374 molecular diffusion 241, 374 surface diffusion 304, 374 diffusion volumes 493, 504, 562 dimensional analysis 524, 525 dimensionless groups 525–528, 538, 539, 543, 548 dimensionless numbers 525 dioxins 18, 25, 347 dipole moment 553 dispersion axial 141–143, 149–151, 154, 169, 183, 184, 215, 416, 530

coefficient 126, 127, 143, 149, 150, 153, 183, 392 gas phase 126–127, 135, 137, 183 liquid phase 126–127 radial 149, 151, 154, 158, 408, 416, 530 distribution coefficient 271 distributor quality 158 drag force 127, 210, 235, 236, 239, 240 Dwivedi and Upadhay equation 165 E Edgeworth and Thring method 527 Edwards–Richardson correlation 151, 327, 330 effective diffusivity 240, 374 effectiveness factor for plate 374–377 for spherical pellet 377–378 internal 413, 446, 450, 470, 479, 504 overall 382–384 external 371–372 El-Hisnawi et al. correlation 180, 426 emulsion phase 191, 207–209, 214, 217, 219, 224, 481, 485, 489, 498 encapsulation 349, 351 endothermic reactions 146, 368, 369, 417 energy of dissipation 135, 136, 402, 403 Eotvos number 125, 156 equilibrium isotherm Dubinin–Raduskevish 269–270 favorable 268–269, 316 Freundlich 267–269, 316–320 irreversible 269 ion exchange 270–272 Langmuir 266–267, 316–320 linear 268–269 sigmoidal 268–269 unfavorable 268–269, 335 Ergun equation 146, 194, 195, 199, 430, 440–442, 459, 474, 476, 477 European Pollutant Emission Register (EPER) 9, 10 eutrophication 18–20, 24 exothermic reactions 102, 146, 367, 368, 417, 529 expansion factor 87–92, 108–110, 147, 173, 309, 407, 418, 420, 433, 449

598

Index

F Fanning friction factor 161, 162 Faraday constant 558, 575 Fick’s law 264–265 Field and Davidson equation 127, 392 fixed bed adiabatic operation modeling 407, 416 isothermal operation modeling 407, 408 modeling (general) 141–148 monolithic catalysts 407, 421 nonisobaric operation 407, 427 reactors (general) 74, 77 voidage 63, 93, 143, 147, 155, 157, 312, 430 wetting efficiency 148 fluidization regimes 190–193 type A 193, 200–205, 213, 496 type B 193, 200–205, 213, 217, 481 fluidized bed models Chavarie–Grace model 487, 492, 497, 498 Kelkar–Ng model 486, 492, 498 Kunii–Levenspiel model 174, 207–211, 220, 226, 487–488, 492, 495, 496, 498, 499 Orcutt–Davidson–Pigford model 472, 481, 482, 491, 492, 498 two-phase model 200, 207, 217, 227, 481, 496, 506, 545 fluidized bed voidage 199, 216, 223 Foscolo–Gibilaro equation 203, 240 Froude number 126, 206, 528, 542, 549 Fuller–Schettler–Giddings equation 493, 561 G Galileo number 186, 462, 476 Garner–Suckling correlation 163 gas distribution 116–117, 162, 184–185, 216–217 Geldart’s classification of powders 191–193 Goto and Smith equation 186 greenhouse effect 1, 2, 5, 22 H Hayduk and Laudie equation 463, 559 heat transfer coefficient 146, 165, 189, 224, 367, 369, 373, 578 external 367–373

internal 373–384 rate 102, 140, 371, 373 heavy metals 18, 23–25, 28, 223, 250, 251, 260, 303, 307, 315, 324, 327, 347, 521 Helfferich–Glueckauf model 332, 333, 344 hemoglobin 22, 503 Henry’s constant 70, 112, 405, 564–569 Hinshelwood approach 359 Hochman–Effron correlation 183 holding time 92 hold up gas 116, 118–122, 128, 136, 137, 179, 403 liquid 63, 121, 128, 147, 155–157, 178, 308, 343, 395, 398, 402, 532, 535 solid 121, 128, 209, 485 Hougen and Watson approach 359 hydraulic diameter of the particles 155 hydrocarbons 5, 22, 24, 26, 28, 50–53, 166, 243, 45, 251, 326, 352, 513 hydrogen peroxide 351, 461, 463, 465, 560 I immobilization 349–352 impeller critical speed 135 discharge coefficient 94, 95 discharge rate 94, 95 power number 94, 95, 97, 98, 549 Reynolds number 81, 94, 98, 549 impellers axial-flow 79–80 disc-type turbines 81 flat-blade radial-flow 80 open-type turbines 80–81 pitched blade turbine 80 propeller 79–80 radial-flow 80–82 Rushton 81–82 inorganic salts and acids 25 interfacial area 128–130, 180, 185, 186, 188, 293, 393 interstitial velocity 118, 392 ion exchange advantages 50 definition 33–34 disadvantages 50 environmental applications 28, 49–50 historical aspects 38–39

Index

599

ion exchange materials composite 261–262 natural inorganic 253–255 natural organic 255 synthetic inorganic 255–256 synthetic organic 256–257 ionic conductances 558 ionic mobility 558, 559 isobaric operation 88, 89, 309 isothermal operation 89, 146, 147, 169, 217, 309, 419, 421, 464, 480 isotopic exchange 264, 274–276, 279, 282, 283 K Kataoka correlation 163 Koloini–Sospic–Zumer correlation 225 L Lagergren’s rate equation 288 lambda sensor 53 Larkins equation 176, 177, 459 Levins and Glastonbuty correlation 101, 138, 301 Limal–Ballesteros correlation 198, 508 limiting reactant 85–88, 104, 105, 108, 109, 134, 173, 182, 387–389, 406, 412, 428, 540 linear driving force 309, 312, 313, 334 linear velocity 311, 338, 339, 342, 533, 534, 536, 537 liquid distribution 158, 159, 184, 185, 523, 540 M Mackie and Meares equation 242 maldistribution 154, 158, 160, 182, 184, 308, 309, 534, 536, 539 mass transfer coefficient 100, 101, 127–130, 138–139, 162, 185, 187, 293, 303, 367, 373, 393, 404, 479 external 100–101, 127–131, 138–139, 185–189, 223–227, 409, 413 internal 167, 358, 373, 374, 408, 410, 413 maximum bed height 536, 537 maximum exchange level 268, 301 maximum working velocity 523, 536, 537 mercury 5, 12, 20, 23–26, 46, 245, 261

methyl tert butyl ether (MTBE) 19, 246 mean bubble rise velocity 126, 392 Mears criterion 183, 462, 463, 471, 476, 478 mechanical parameter 290, 324, 326, 338, 510 mechanical strength 46, 227, 231 minimum bubbling velocity 191, 193, 201, 202, 207, 495, 496, 501, 506 minimum fluidization velocity 191, 193, 195–197, 210, 222, 482, 536, 543 minimum voidage at incipient fluidization 197–198 Miura and Hashimoto criterion 510 model 316–320 Mochizuki–Matsui correlation 187, 471, 479 mock-ups 531, 538, 539 molar volume 270, 463, 464, 554, 559, 560, 569–570 monolith 52, 74, 351, 355, 407, 421, |422, 517, 538 Mori–Wen correlation 211, 494, 505 N Nernst–Haskell equation 306, 558 Nernst–Haskell model 280, 282 Newton’s law 237 ⫺ ⫺ 3⫺ NH⫹ 4 , NO 3 , NO 2 , PO 4 ions 24 nitrogen compounds 22 nitrogen dioxide 2, 3, 8, 11, 22 nitrogen oxide 3, 22, 36 nitrogen oxides 3, 5, 9, 14, 22, 24, 28, 51–53 nitrous oxide 22, 24, 503, 504, 507, 560, 562 nonisobaric operation 88, 197, 309, 407, 418, 427 nonisothermal operation 88, 309, 418, 428 nonmethane volatile organic compounds 5, 6, 9 O operating capacity 312, 334 organically modified clays 248 organic compounds 25, 244, 247, 269, 566, 567, 569, 570

600

Index

ozone depletion 2, 5, 6, 22 destruction mechanism 5, 36 stratospheric 1, 5 tropospheric 4, 5, 22 P packed bubble bed reactor 77, 168, 539 modeling 174, 176, 394, 398, 449, 471 particle diameter 150, 229, 536, 539, 542, 544 particulate fluidization 191, 193, 199, 202, 205, 206, 215, 223, 224, 481 particulate matter 2, 3, 5, 23, 24, 27 partition ratio 273 pathogenic microorganisms 26 Patterson’s equation 277, 282 Pavlov’s equation 200 Peclet number 150, 151, 153, 183, 184, 215, 216, 311, 535 perforated pipe distributors 360–362 phenol 46, 258, 315, 328, 331, 352, 469–473, 513, 518, 561, 564, 568–572 plug flow assumption/condition 105, 112, 115, 144, 148–151, 169, 175, 183, 208, 214, 219–223, 311, 312, 334 polychlorinated biphenyls 5, 18, 25 polymeric resin adsorbents 248 pore-size distribution 230 porosity aggregate 233 bed 63, 197, 540 fluidized bed 193, 198, 481 internal 251, 253, 232, 241 macropores 32, 33, 230 mesopores 32, 33, 230 micropores 32, 33, 230 microporosity 33 particle 32, 99, 230, 374 supermicropores 33 ultramicropores 33 pressure drop 146–147, 157, 160–162, 176, 177, 185, 194, 196, 216, 239–240, 309, 355, 407, 421, 427–449, 456, 458–458, 476–478, 530, 533, 537 process development unit (PDU) 529

R radial mixing 140, 149, 151, 215, 216, 416 rate coefficient 59–62, 64, 108, 367, 465, 470, 474 rate-controlling step 68–69, 273, 289–293, 415 rates catalytic reaction rate 57, 62, 359 catalytic reaction rate law derivation 362–363 global rate 66, 102, 104, 371, 372, 408, 410, 418, 425 intrinsic reaction rate 60, 68, 415, 426, 428, 429, 488 observed reaction rate 373, 380 overall reaction rate 66, 67, 113, 174, 359, 413, 415, 429 per unit mass of catalyst 133, 171, 360–362, 369, 373, 408, 423, 425 per unit mass of the solid phase 57, 86, 144 per unit surface of the solid 58 per unit volume of reactor 57, 62, 145, 171, 220, 428, 429 per unit volume of the fluid phase 62, 85, 86 per unit volume of the solid phase 58 phenomenological rates 62 rate of attachment 359, 360 rate of detachment 360 Rayleigh–Taylor instability 124 reactivation 347 reactors agitated slurry 78, 102, 131, 398, 548 batch agitated 74, 79, 84, 85, 87 batch stirred tank 73 Carberry 74, 75 continuous stirred tank 73 entrained flow 76 fixed bed 74, 75, 77, 139, 407, 531 fluidized bed 76, 101, 189, 481, 540 gas-liquid bubble column 74 gas-liquid continuous stirred tank 73–74 monolith 74, 421, 538 moving bed 75 packed bubble bed 77, 106, 168, 176 plug flow tubular 73 semibatch 74, 78, 104–106 slurry bubble column 78, 104, 386, 548 three-phase fluidized bed 77 trickle bed 77, 166, 449, 539

Index

601

real exchange capacity 268 recycle reactors 472 regeneration activated carbon 347 adsorbents 252, 347 ion exchange regeneration 257, 260, 348–349 pressure swing adsorption 347, 348 thermal swing regeneration 348 vacuum regeneration 348 Reiss correlation 187, 479 relative volumetric flow rate 93, 342, 343, 533 residence time 92, 93, 118, 148, 167, 169, 225, 309, 311, 316, 340, 406, 501, 536, 539 Richardson–Zaki equation 203 S scale-up agitated vessels 548–550 definition 523 experimental scale-up 529–531 fixed beds 531–539 fluidized beds 540–548 mathematical modeling 523–524 physical modeling 524–528 slurry bubble bed column reactors 548 trickle bed reactors 539–540 scale-up methods Gliksman’s approach 542 Horio’s approach 543 scaling laws 541–543 Schmidt number 165, 323, 325, 527 selectivity catalysis 35, 36, 51, 356, 512, 538 ion exchange 46, 260, 261, 292, 295, 307 reaction 487 selectivity coefficient 272 separation factor 269–271, 291, 332 shape factor impeller 99, 549 particle 128, 138 Sherwood number 164, 165, 527 shrinking core model 266, 283, 293 silica gel 47, 231, 241, 243, 247, 248, 251, 252, 356, 357 similarity criteria 525, 528

dynamic 308, 524 geometrical 308, 524, 531 hydrodynamic 543, 544, 546, 548 kinematic 308, 524 single bubble rising velocity 125, 392 slugging fluidization 191, 206–207 smog photochemical 2, 3, 10, 15, 22, 23, 51 summer-type 3 winter-type 3, 22, 23 solubility 105, 109, 166, 174, 245, 246, 518, 564–569 sorption chemical 31 electrostatic 31 physical 31 space time 92, 93 space velocity 92, 93 specific heat capacity 165, 224 sphericity of the particle 146, 195, 228, 229, 290, 430, 542 Stiegel–Shah correlation 184, 479 stoichiometric point 337, 338 Stoke’s law 233, 234, 237 superficial velocity fixed beds 146, 328, 338, 407, 430, 443, 533–534 fluidized beds 189, 205, 482, 542–543 slurry reactors 114, 118 trickle beds 181–182, 540 surface aeration 82, 139, 301 surface tension 32, 118, 124, 139, 549, 575, 577 swelling 34, 46, 230, 235, 256, 258, 284, 351, 352 T terminal settling velocity 128 terminal velocity 100, 203, 227, 234–237 thermal conductivity 146, 165, 224 Thiele modulus 376, 377, 378, 380, 381, 413, 464, 480 Thomas model 327, 328, 336 tortuosity 241, 242, 461, 465 tortuosity factor 153, 240, 241, 374 Toxic Release Inventory (TRI) 8, 9 transitional fluidization 206 treatment methods 27–28

602

Index

trickle bed reactors Fogler’s approach 174–176, 456–458 hydraulic regimes 166–168 modeling 169–176 Smith’s approach 169–174, 449–456 reactor (general) 77, 166 turbulence intensity 127 turnover frequency 58, 59, 61, 65–66 Tyn and Calus equation 559 U unbaffled tanks 82–84, 95, 100 utilization (column/fixed bed) 335, 344–346 V vapor pressure 231, 269, 329, 348, 565, 569 variable-volume systems 87, 144, 385, 386, 412, 456, 458 Vermeulen’s approximation 275, 282 viscosity dynamic 146, 178, 180, 181, 430, 526 kinematic 98, 101, 138, 165, 526 volatile organic compounds (VOCs) 3, 22–24 volume-surface mean diameter of bubbles 124 W Wakao equation 164 water distribution 16–17 drinking 16, 17 fresh 16, 17 quality deterioration 18 salty 16 use 16

water pollutants heavy metals 18, 23–25 inorganic salts and acids 25 3⫺ NH⫹4 , NO⫺3 , NO⫺ 2 , PO 4 ions 24 oil compounds 25 organic compounds 25–26 pathogenic microorganisms 26 water pollution Asia 20–21 Australia and New Zealand 20 biological 17–18 by inorganic pollutants 18–19 by persistent organic chemicals 18 Europe 19 eutrophication 18 Japan 20 Latin America 20 North America 19 thermal 17 water releases main source 24 nonpoint sources 23 point sources 23 Weisz and Prater criterion 379 wetting efficiency 179–182 Wheeler–Jonas model 328 Williamson correlation 163 Wilson–Geankoplis correlation 163 Z zeolite molecular sieves 248–250 zeolites 38, 40, 44–47, 150, 156, 157, 248, 249, 251–253, 255, 257, 307, 311, 356, 534 Zwietering equation 98

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